To see the other types of publications on this topic, follow the link: Graphical model.
Dissertations / Theses on the topic 'Graphical model'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 dissertations / theses for your research on the topic 'Graphical model.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse dissertations / theses on a wide variety of disciplines and organise your bibliography correctly.
1
Kamenetsky, Dmitry, and dkamen@rsise anu edu au. "Ising Graphical Model." The Australian National University. ANU College of Engineering and Computer Science, 2010. http://thesis.anu.edu.au./public/adt-ANU20100727.221031.
Full textAbstract:
The Ising model is an important model in statistical physics, with over 10,000 papers published on the topic. This model assumes binary variables and only local pairwise interactions between neighbouring nodes. Inference for the general Ising model is NP-hard; this includes tasks such as calculating the partition function, finding a lowest-energy (ground) state and computing marginal probabilities. Past approaches have proceeded by working with classes of tractable Ising models, such as Ising models defined on a planar graph. For such models, the partition function and ground state can be computed exactly in polynomial time by establishing a correspondence with perfect matchings in a related graph.
In this thesis we continue this line of research. In particular we simplify previous inference algorithms for the planar Ising model. The key to our construction is the complementary correspondence between graph cuts of the model graph and perfect matchings of its expanded dual. We show that our exact algorithms are effective and efficient on a number of real-world machine learning problems. We also investigate heuristic methods for approximating ground states of non-planar Ising models. We show that in this setting our approximative algorithms are superior than current state-of-the-art methods.
2
Smith, Peter William Frederick. "Edge exclusion and model selection in graphical models." Thesis, Lancaster University, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.315138.
Full text
3
Lin, Jiali. "Bayesian Multilevel-multiclass Graphical Model." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/101092.
Full textAbstract:
Gaussian graphical model has been a popular tool to investigate conditional dependency between random variables by estimating sparse precision matrices. Two problems have been discussed. One is to learn multiple Gaussian graphical models at multilevel from unknown classes. Another one is to select Gaussian process in semiparametric multi-kernel machine regression.
The first problem is approached by Gaussian graphical model. In this project, I consider learning multiple connected graphs among multilevel variables from unknown classes. I esti- mate the classes of the observations from the mixture distributions by evaluating the Bayes factor and learn the network structures by fitting a novel neighborhood selection algorithm. This approach is able to identify the class membership and to reveal network structures for multilevel variables simultaneously. Unlike most existing methods that solve this problem by frequentist approaches, I assess an alternative to a novel hierarchical Bayesian approach to incorporate prior knowledge.
The second problem focuses on the analysis of correlated high-dimensional data which has been useful in many applications. In this work, I consider a problem of detecting signals with a semiparametric regression model which can study the effects of fixed covariates (e.g. clinical variables) and sets of elements (e.g. pathways of genes). I model the unknown high-dimension functions of multi-sets via multi-Gaussian kernel machines to consider the possibility that elements within the same set interact with each other. Hence, my variable selection can be considered as Gaussian process selection. I develop my Gaussian process selection under the Bayesian variable selection framework.Doctor of Philosophy
4
Desana, Mattia [Verfasser], and Christoph [Akademischer Betreuer] Schnörr. "Sum-Product Graphical Models: a Graphical Model Perspective on Sum-Product Networks / Mattia Desana ; Betreuer: Christoph Schnörr." Heidelberg : Universitätsbibliothek Heidelberg, 2018. http://d-nb.info/1177044358/34.
Full text
5
Schmidt, Mark. "Graphical model structure learning using L₁-regularization." Thesis, University of British Columbia, 2010. http://hdl.handle.net/2429/27277.
Full textAbstract:
This work looks at fitting probabilistic graphical models to data when the structure is not known. The main tool to do this is L₁-regularization and the more general group L₁-regularization. We describe limited-memory quasi-Newton methods to solve optimization problems with these types of regularizers, and we examine learning directed acyclic graphical models with L₁-regularization, learning undirected graphical models with group L₁-regularization, and learning hierarchical log-linear models with overlapping group L₁-regularization.
6
Seward, D. C. (DeWitt Clinton). "Graphical analysis of hidden Markov model experiments." Thesis, Massachusetts Institute of Technology, 1994. http://hdl.handle.net/1721.1/36469.
Full textAbstract:
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1994.Includes bibliographical references (leaves 60-61).
by DeWitt C. Seward IV.
Ph.D.
7
Pu, Yewen. "A novel inference algorithm on graphical model." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/97818.
Full textAbstract:
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.Cataloged from PDF version of thesis.
Includes bibliographical references (pages 57-58).
We present a framework for approximate inference that, given a factor graph and a subset of its variables, produces an approximate marginal distribution over these variables with bounds. The factors of the factor graph are abstracted as as piecewise polynomial functions with lower and upper bounds, and a variant of the variable elimination algorithm solves the inference problem over this abstraction. The resulting distributions bound quantifies the error between it and the true distribution. We also give a set of heuristics for improving the bounds by further refining the binary space partition trees.
by Yewen Pu.
S.M.
8
Cooke, Christopher Alexander. "Interactive graphical model building using virtual reality." Thesis, Massachusetts Institute of Technology, 1994. http://hdl.handle.net/1721.1/34065.
Full textAbstract:
Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1994.Includes bibliographical references (leaves 58-59).
by Christopher Alexander Cooke.
M.S.
9
Jammalamadaka, Arvind K. (Arvind Kumar) 1981. "Aspects of inference for the Influence Model and related graphical models." Thesis, Massachusetts Institute of Technology, 2004. http://hdl.handle.net/1721.1/28557.
Full textAbstract:
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.Includes bibliographical references (p. 61-64).
The Influence Model (IM), developed with the primary motivation of describing network dynamics in power systems, has proved to be very useful in a variety of contexts. It consists of a directed graph of interacting sites whose Markov state transition probabilities depend on their present state and that of their neighbors. The major goals of this thesis are (1) to place the Influence Model in the broader framework of graphical models, such as Bayesian networks, (2) to provide and discuss a hybrid model between the IM and dynamic Bayesian networks, (3) to discuss the use of inference tools available for such graphical models in the context of the IM, and (4) to provide some methods of estimating the unknown parameters that describe the IM. We hope each of these developments will enhance the use of IM as a tool for studying networked interact ions.
by Arvind K. Jammalamadaka.
S.M.
10
Komodakis, Nikos. "Graphical Model Inference and Learning for Visual Computing." Habilitation à diriger des recherches, Université Paris-Est, 2013. http://tel.archives-ouvertes.fr/tel-00866078.
Full textAbstract:
Computational vision and image analysis is a multidisciplinary scientific field that aims to make computers "see" in a way that is comparable to human perception. It is currently one of the most challenging research areas in artificial intelligence. In this regard, the extraction of information from the vast amount of visual data that are available today as well as the exploitation of the resulting information space becomes one of the greatest challenges in our days. To address such a challenge, this thesis describes a very general computational framework that can be used for performing efficient inference and learning for visual perception based on very rich and powerful models.
11
Yellepeddi, Atulya. "Graphical model driven methods in adaptive system identification." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/107499.
Full textAbstract:
Thesis: Ph. D., Joint Program in Applied Ocean Science and Engineering (Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science; and the Woods Hole Oceanographic Institution), 2016.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 209-225).
Identifying and tracking an unknown linear system from observations of its inputs and outputs is a problem at the heart of many different applications. Due to the complexity and rapid variability of modern systems, there is extensive interest in solving the problem with as little data and computation as possible. This thesis introduces the novel approach of reducing problem dimension by exploiting statistical structure on the input. By modeling the input to the system of interest as a graph-structured random process, it is shown that a large parameter identification problem can be reduced into several smaller pieces, making the overall problem considerably simpler. Algorithms that can leverage this property in order to either improve the performance or reduce the computational complexity of the estimation problem are developed. The first of these, termed the graphical expectation-maximization least squares (GEM-LS) algorithm, can utilize the reduced dimensional problems induced by the structure to improve the accuracy of the system identification problem in the low sample regime over conventional methods for linear learning with limited data, including regularized least squares methods. Next, a relaxation of the GEM-LS algorithm termed the relaxed approximate graph structured least squares (RAGS-LS) algorithm is obtained that exploits structure to perform highly efficient estimation. The RAGS-LS algorithm is then recast into a recursive framework termed the relaxed approximate graph structured recursive least squares (RAGS-RLS) algorithm, which can be used to track time-varying linear systems with low complexity while achieving tracking performance comparable to much more computationally intensive methods. The performance of the algorithms developed in the thesis in applications such as channel identification, echo cancellation and adaptive equalization demonstrate that the gains admitted by the graph framework are realizable in practice. The methods have wide applicability, and in particular show promise as the estimation and adaptation algorithms for a new breed of fast, accurate underwater acoustic modems. The contributions of the thesis illustrate the power of graphical model structure in simplifying difficult learning problems, even when the target system is not directly structured.
by Atulya Yellepeddi.
Ph. D.
12
Rahme, Youssef. "Stochastic matching model on the general graphical structures." Thesis, Compiègne, 2021. http://www.theses.fr/2021COMP2604.
Full textAbstract:
Motivé par des applications à large éventail des systèmes d’assemblage à la commande et des systèmes de l’économie collaborative, nous introduisons un modèle d’appariement aléatoire sur les hypergraphes et sur les multigraphes, étendant le modèle par Mairesse et Moyal 2016. Dans cette thèse, le modèle d’appariement aléatoire sur les structures graphiques générales est défini comme suit : étant donné une structure graphique générale de compatibilité S = (V; S) qui est constituée d’un ensemble de nœuds noté par V qui représentent les classes d’éléments et par un ensemble d’arêtes noté par S qui permettent d’apparier entre les différentes classes. Les éléments arrivent au système à un moment aléatoire, par une séquence (supposée être i:i:d:) constituée de différentes classes de V; et demandent d’être appariés selon leur compatibilité dans S: La compatibilité par groupe de deux ou plus (cas hypergraphique) et par groupe de deux avec les possibilités d’apparier entre les éléments de même classe (cas multigraphique). Les éléments, qui ne sont pas appariés, sont stockés dans le système et en attente d’un futur élément compatible et dès qu’ils sont appariés, ils quittent le système ensemble. À l’arrivée, un élément peut trouver plusieurs d’appariements possibles, les éléments qui quittent le système dépendent d’une politique d’appariement Ø à spécifier. Nous étudions la stabilité du modèle d’appariement aléatoire sur l’hypergraphe, pour des différentes topologies hypergraphiques puis, la stabilité du modèle d’appariement aléatoire sur les multigraphes en utilisant son sous-graphe maximal et sur-graphe minimal étendu pour distinguer la zone de stabilitéMotivated by a wide range of assemble-to-order systems and systems of the collaborativeeconomy applications, we introduce a stochastic matching model on hypergraphs and multigraphs, extending the model introduced by Mairesse and Moyal 2016. In this thesis, the stochastic matching model on general graph structures are defined as follows: given a compatibility general graph structure S = (V; S) which of a set of nodes denoted by V that represent the classes of items and by a set of edges denoted by S that allows matching between different classes of items. Items arrive at the system at a random time, by a sequence (assumed to be i:i:d:) that consists of different classes of V; and request to be matched due to their compatibility according to S: The compatibility by groups of two or more (hypergraphical cases) and by groups of two with possibilities of matching between the items of the same classes (multigraphical cases). The unmatched items are stored in the system and wait for a future compatible item and as soon as they are matched they leave it together. Upon arrival, an item may find several possible matches, the items that leave the system depend on a matching policy _ to be specified. We study the stability of the stochastic matching model on hypergraphs, for different hypergraphical topologies. Then, the stability of the stochastic matching model on multigraphs using the maximal subgraph and minimal blow-up to distinguish the zone of stability
13
Lartigue, Thomas. "Mixtures of Gaussian Graphical Models with Constraints Gaussian Graphical Model exploration and selection in high dimension low sample size setting." Thesis, Institut polytechnique de Paris, 2020. http://www.theses.fr/2020IPPAX034.
Full textAbstract:
La description des co-variations entre plusieurs variables aléatoires observées est un problème délicat. Les réseaux de dépendance sont des outils populaires qui décrivent les relations entre les variables par la présence ou l’absence d’arêtes entre les nœuds d’un graphe. En particulier, les graphes de corrélations conditionnelles sont utilisés pour représenter les corrélations “directes” entre les nœuds du graphe. Ils sont souvent étudiés sous l’hypothèse gaussienne et sont donc appelés “modèles graphiques gaussiens” (GGM). Un seul réseau peut être utilisé pour représenter les tendances globales identifiées dans un échantillon de données. Toutefois, lorsque les données observées sont échantillonnées à partir d’une population hétérogène, il existe alors différentes sous-populations qui doivent toutes être décrites par leurs propres graphes. De plus, si les labels des sous populations (ou “classes”) ne sont pas disponibles, des approches non supervisées doivent être mises en œuvre afin d’identifier correctement les classes et de décrire chacune d’entre elles avec son propre graphe. Dans ce travail, nous abordons le problème relativement nouveau de l’estimation hiérarchique des GGM pour des populations hétérogènes non labellisées. Nous explorons plusieurs axes clés pour améliorer l’estimation des paramètres du modèle ainsi que l’identification non supervisee des sous-populations. ´ Notre objectif est de s’assurer que les graphes de corrélations conditionnelles inférés sont aussi pertinents et interprétables que possible. Premièrement - dans le cas d’une population simple et homogène - nous développons une méthode composite qui combine les forces des deux principaux paradigmes de l’état de l’art afin d’en corriger les faiblesses. Pour le cas hétérogène non labellisé, nous proposons d’estimer un mélange de GGM avec un algorithme espérance-maximisation (EM). Afin d’améliorer les solutions de cet algorithme EM, et d’éviter de tomber dans des extrema locaux sous-optimaux quand les données sont en grande dimension, nous introduisons une version tempérée de cet algorithme EM, que nous étudions théoriquement et empiriquement. Enfin, nous améliorons le clustering de l’EM en prenant en compte l’effet que des cofacteurs externes peuvent avoir sur la position des données observées dans leur espaceDescribing the co-variations between several observed random variables is a delicate problem. Dependency networks are popular tools that depict the relations between variables through the presence or absence of edges between the nodes of a graph. In particular, conditional correlation graphs are used to represent the “direct” correlations between nodes of the graph. They are often studied under the Gaussian assumption and consequently referred to as “Gaussian Graphical Models” (GGM). A single network can be used to represent the overall tendencies identified within a data sample. However, when the observed data is sampled from a heterogeneous population, then there exist different sub-populations that all need to be described through their own graphs. What is more, if the sub-population (or “class”) labels are not available, unsupervised approaches must be implemented in order to correctly identify the classes and describe each of them with its own graph. In this work, we tackle the fairly new problem of Hierarchical GGM estimation for unlabelled heterogeneous populations. We explore several key axes to improve the estimation of the model parameters as well as the unsupervised identification of the sub-populations. Our goal is to ensure that the inferred conditional correlation graphs are as relevant and interpretable as possible. First - in the simple, homogeneous population case - we develop a composite method that combines the strengths of the two main state of the art paradigms to correct their weaknesses. For the unlabelled heterogeneous case, we propose to estimate a Mixture of GGM with an Expectation Maximisation (EM) algorithm. In order to improve the solutions of this EM algorithm, and avoid falling for sub-optimal local extrema in high dimension, we introduce a tempered version of this EM algorithm, that we study theoretically and empirically. Finally, we improve the clustering of the EM by taking into consideration the effect of external co-features on the position in space of the observed data
14
PLAKSIENKO, ANNA. "Joint estimation of multiple graphical models." Doctoral thesis, Gran Sasso Science Institute, 2021. http://hdl.handle.net/20.500.12571/21632.
Full textAbstract:
The fast development of high-throughput technologies such as microarray or next-generation sequencing, and the consequent in-depth investigation of the genome in several international large scale projects, have led to the generation of large amounts of high-dimensional omics datasets. Scientists can use such data to acquire a deep understanding of complex cellular mechanisms, the molecular basis of diseases’ development, etc. Among other questions, relationships between different genes or other similar units can reveal regulatory mechanisms whose disruption can be associated with diseases. Network inference methods and, more specifically, graphical models estimation can be used to identify gene relationships and direct interactions not mediated by other factors.
Simply speaking, a graphical model is a graph whose vertices correspond to random variables and edges denote conditional dependence relationships between them. There are plenty of methods for carrying out graphical model inference from a given dataset, even in the high-dimensional setting where the number of variables is much larger than the number of samples (a common situation in omics studies for the enormous number of genes involved and a limited number of samples collected). However, nowadays, it is common to collect and analyze more than one dataset. Multiple datasets can be obtained in different laboratories or with different technologies, arise from various studies, or be of different omics types. Their joint analysis can lead to a more accurate characterization of the underlying biological system, but it also requires specific techniques.
In this thesis, we propose jewel – a novel method for the joint analysis of multiple datasets under the assumption that they are drawn from Gaussian distributions that share the same network dependency. In this context, the conditional dependence relationships between variables (genes) are encoded by the inverse covariance matrix. Although we assume that the conditional dependence structure is the same between different conditions, we let the covariance matrices be different to account for different sources of data origin. In this setting, combining the individual datasets into a single one and estimating a sole graphical model would mask the covariance matrices’ heterogeneity, while estimating separate models for each case would not take advantage of the common underlying structure. Therefore, a joint analysis of the datasets is preferable, and to this aim in this thesis we present a novel joint estimation method jewel. It extends the Meinshausen and Bühlmann regression-based approach to the case of multiple datasets by the mean of a group lasso penalty which guarantees the symmetry of the solution. We design a fast algorithm for the method’s implementation, incorporating the smart active shooting approach for a fixed regularization parameter and the warm start approach for an entire grid of regularization parameters. We also state a theorem for jewel’s consistency, providing upper and lower bounds for regularization parameter. Moreover, we extend the Bayesian information criterion and cross-validation procedures to the multiple datasets framework to provide a practical tool for real case applications. We explore the behavior of jewel in different simulation settings, analyzing the influence of various input parameters, and comparing the method to other available alternatives for joint estimation, revealing good and competitive performances. Finally, we illustrate the method’s performance in real data example regarding transcriptional regulatory networks based on gene expression data. We implement the proposed method in the novel R package jewel.
15
Srinivasan, Vivekanandan. "Real delay graphical probabilistic switching model for VLSI circuits." [Tampa, Fla.] : University of South Florida, 2004. http://purl.fcla.edu/fcla/etd/SFE0000538.
Full text
16
Moukbel, Mehiar. "MBVC – Model Based Version Control : An Application of Configuration Management on Graphical Models." Thesis, KTH, Maskinkonstruktion (Inst.), 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-100813.
Full textAbstract:
Filbaserad versionshantering är ett verktyg inom mjukvaruutvecklingen, och det existerar ett stort utbud av kommersiella produkter. Problemet är dock att de flesta verktygen fungerar endast för textbaserade filer, och saknar någon motsvarighet till hantering av ’fine grained’ filer som exemplevis grafiska Simulink modeller. Eftersom Simulink är ett utspritt modelleringsvertyg och används inom flera utvecklingsarbeten och särskillt inom mekatronik, så är det intressant att studera möjligheten att utveckla ett sådant verktyg. Genom analys av två tillgängliga konfigurationsverktyg: CVS och Rational Clear Case, samt studie av diverse publikationer och rapporter av versionshantering och algoritmer angående ’ diff’ och ’merge ’ funktioner, så utvecklades ett enkelt sådant verktyg. Programmet utför enkel skillnads- och föreneingsfunktioner (2-way merge) på grafiska Simulink modeller. Verktyget fungerade inte som det var uttänkt i början men det lyckades ändå visa skillnader mellan Simulink modellerna både grafiskt och textmässigt. Ett tredje verktyg, Rhapsody, som används inom MDD studerades, samt dess samarbete med Simulnik testades. Resultatet visar att programmens samverkan är möjlig men något komplex och kräver erfarenheter från båda programmen. Studien visar att det går att bygga ett mer avancerat konfigurations-hanteringsprogram för Simulink modeller, såsom ett 3-way merge, men vissa svårigheter som en korretk koppling av blocken måste först lösas. .File-based version control consists of tools in the software engineering industry, with many available commercial products that allow multiple developers to work simultaneously on a single project. However these tools are most commonly used on plain textual documents such as source code. There exist few tools today for versioning fine-grained data such as graphical Simulink models. Since Simulink is widely used as a modeling tool in numerous engineering fields, nonetheless in the mechatronics field, it will be interesting to study the possibility of developing a tool for version control of graphical models. Two textual software configuration management (SCM) products, CVS and Rational Clear Case, were studied and their functionalities were analyzed, along with a different number of research topics on document versioning. The existing algorithms of ‘ diff ’ and ‘ merge ’ functions were also studied to give an understanding of how these functions work for text based documents. The knowledge gained from the tools, existing algorithms and literature on the subject were used to write MATLAB programs that perform diff and merge on Simulink models. The resulted programs perform 2-way diff and merge on Simulink models and display the differences graphically using color codes. Although the tool did have some limitations and did not perform all the expected SCM functions, it still displayed differences between Simulink models. Displaying of results occurred both graphically and textually. A third tool called Rhapsody was studied which is used in model driven development and its interaction with Simulink was also studied, showing that is possible but rather complex and requires knowledge in both programs. The study shows thus that it is possible to build and develop configuration management tools for graphical models in Simulink, possibly also the 3-way merges, but certain difficulties such as connecting blocks correctly must firstly be solved.
17
PETRAKIS, NIKOLAOS. "Objective Bayes Structure Learning in Gaussian Graphical Models." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2020. http://hdl.handle.net/10281/262921.
Full textAbstract:
Graphical models are used to represent conditional independence relationships among variables by the means of a graph, with variables corresponding to graph's nodes. They are widely used in genomic studies, finance, energy forecasting, among other fields. More specifically, for a collection of q variables with conditional independence structure represented by an undirected graph, we assume that the underlying graph's structure is unknown. We are interested in inferring the graph's structure from data at hand. This procedure the bibliography is referred to as Structure Learning, where we use certain techniques for selecting a graphical model to depict conditional independence relationships between these q variables. We start from defining a model space which is consisted by a set of all possible graphical models; then we define a scoring function which enables us to score the different models of the model space and finally, we construct a search algorithm that will navigate through the model space to identify the optimal model that explains the problem at hand. The choice of a scoring function is crucial for optimizing the search procedure through the model space. Our approach to this problem is purely Bayesian for handling uncertainty in a more elaborate fashion. We will use estimates of posterior model probabilities for ranking the models at hand. The specification of a conditional prior on the column covariance matrix is not trivial because each graph under consideration induces a different independence structure and it affects the parameter space. Under this context, we cannot directly use improper priors, since they would result to indeterminate Bayes factors, thus we are required to carefully elicit a prior distribution under each graph, a task that becomes infeasible in higher dimensions. For creating an automated Bayesian scoring technique, we resort to Objective Bayes approaches, which are initiated by an improper prior distribution and their output is a fully usable prior distributions. In this thesis, we propose the use of two alternative Objective Bayes approaches for estimating posterior probabilities of models, namely the Expected Posterior prior approach and the Power-Expected Posterior Prior approach. Both approaches utilize the device of imaginary observations for providing usable prior distributions and are theoretically sounder than the Fractional Bayes Factor of O'Hagan. Our goal is to introduce both the Expected and Power-Expected Posterior prior approaches to the field of structure learning of undirected graphical models and evaluate their performance using certain stochastic search techniques. Diverse simulation scenarios are considered as well as a real-life data application.Graphical models are used to represent conditional independence relationships among variables by the means of a graph, with variables corresponding to graph's nodes. They are widely used in genomic studies, finance, energy forecasting, among other fields. More specifically, for a collection of q variables with conditional independence structure represented by an undirected graph, we assume that the underlying graph's structure is unknown. We are interested in inferring the graph's structure from data at hand. This procedure the bibliography is referred to as Structure Learning, where we use certain techniques for selecting a graphical model to depict conditional independence relationships between these q variables. We start from defining a model space which is consisted by a set of all possible graphical models; then we define a scoring function which enables us to score the different models of the model space and finally, we construct a search algorithm that will navigate through the model space to identify the optimal model that explains the problem at hand. The choice of a scoring function is crucial for optimizing the search procedure through the model space. Our approach to this problem is purely Bayesian for handling uncertainty in a more elaborate fashion. We will use estimates of posterior model probabilities for ranking the models at hand. The specification of a conditional prior on the column covariance matrix is not trivial because each graph under consideration induces a different independence structure and it affects the parameter space. Under this context, we cannot directly use improper priors, since they would result to indeterminate Bayes factors, thus we are required to carefully elicit a prior distribution under each graph, a task that becomes infeasible in higher dimensions. For creating an automated Bayesian scoring technique, we resort to Objective Bayes approaches, which are initiated by an improper prior distribution and their output is a fully usable prior distributions. In this thesis, we propose the use of two alternative Objective Bayes approaches for estimating posterior probabilities of models, namely the Expected Posterior prior approach and the Power-Expected Posterior Prior approach. Both approaches utilize the device of imaginary observations for providing usable prior distributions and are theoretically sounder than the Fractional Bayes Factor of O'Hagan. Our goal is to introduce both the Expected and Power-Expected Posterior prior approaches to the field of structure learning of undirected graphical models and evaluate their performance using certain stochastic search techniques. Diverse simulation scenarios are considered as well as a real-life data application.
18
Hamidi-Ravari, Omid. "Novel graphical approaches in QCD and the Wess-Zumino model." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp02/NQ36979.pdf.
Full text
19
Hamidi-Ravari, Omid. "Novel graphical approached in QCD and the Wess-Zumino model." Thesis, McGill University, 1997. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=34641.
Full textAbstract:
Quantum Chromodynamics is the underlying theory of hadrons and their interactions. In deriving results from this theory one relies on perturbative calculations. Sometimes indirect methods have been explored to circumvent direct calculation of pure gluonic amplitudes. For example, it has been shown that supersymmetric extension of QCD along with supersymmetric Ward identities can be used to establish relations between amplitudes with the same total number of particles but a different number of gluons. Such relations are used here to connect pure gluonic and pure fermionic amplitudes in the case of 4-pt and 6-pt functions. These relations offer an indirect way of calculating tree level pure gluonic amplitudes since these amplitudes are identical in supersymmetric and non-supersymmetric QCD. The aforementioned relations however, provide no insight into the relation between Feynman diagram of the amplitudes involved. In this regard, we investigate the relation between individual Feynman diagrams in the Wess-Zumino model.Another calculational difficulty arises when one is concerned with high energy scattering in QCD. In the high energy regime, because the effective coupling constant is relatively large, it is necessary to sum up an infinite number of diagrams. This is made even more difficult due to the cancellations in certain color channels that occurs at any perturbative order. The new non-abelian cut diagram technique provides considerable assistance by giving the result with the cancellations already built into its rules. Sixth-order calculations are carried out to show the efficiency of this technique. Finally, we consider the question of diagram with fermion loops that need regularization because of their UV divergence. We find that regularization leads to an enhancement in their high energy behavior.
20
Butcher, Michael David. "A graphical interface model for an electronic office information system." Thesis, Swansea University, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.254957.
Full text
21
Wang, Chaohui. "Distributed and Higher-Order Graphical Models : towards Segmentation, Tracking, Matching and 3D Model Inference." Phd thesis, Ecole Centrale Paris, 2011. http://tel.archives-ouvertes.fr/tel-00658765.
Full textAbstract:
This thesis is devoted to the development of graph-based methods that address several of the most fundamental computer vision problems, such as segmentation, tracking, shape matching and 3D model inference. The first contribution of this thesis is a unified, single-shot optimization framework for simultaneous segmentation, depth ordering and multi-object tracking from monocular video sequences using a pairwise Markov Random Field (MRF). This is achieved through a novel 2.5D layered model where object-level and pixel-level representations are seamlessly combined through local constraints. Towards introducing high-level knowledge, such as shape priors, we then studied the problem of non-rigid 3D surface matching. The second contribution of this thesis consists of a higher-order graph matching formulation that encodes various measurements of geometric/appearance similarities and intrinsic deformation errors. As the third contribution of this thesis, higher-order interactions were further considered to build pose-invariant statistical shape priors and were exploited for the development of a novel approach for knowledge-based 3D segmentation in medical imaging which is invariant to the global pose and the initialization of the shape model. The last contribution of this thesis aimed to partially address the influence of camera pose in visual perception. To this end, we introduced a unified paradigm for 3D landmark model inference from monocular 2D images to simultaneously determine both the optimal 3D model and the corresponding 2D projections without explicit estimation of the camera viewpoint, which is also able to deal with misdetections/occlusions
22
Calargun, Canku Alp. "Dynamic Model Integration And 3d Graphical Interface For A Virtual Ship." Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/12609309/index.pdf.
Full textAbstract:
This thesis addresses the improvement of a physically based modeling simulator Naval
Surface Tactical Maneuvering Simulation System (NSTMSS), that combines different simulators
in a distributed environment by the help of High Level Architecture (HLA), to be
used in naval tactical training systems. The objective is to upgrade a computer simulation
program in which physical models are improved in order to achieve a more realistic movement
of a ship in a virtual environment. The simulator will also be able to model the ocean
waves and ship wakes for a more realistic view. The new naval model includes a 4 degrees
of freedom (DOF) maneuvering model, and a wave model. The numerical results from real
life are used for modeling purposes to increase the realism level of the simulator. Since the
product at the end of the thesis work is needed to be a running computer code that can
be integrated into the NSTMSS system, the code implementation and algorithm details are
also covered. The comparisons between the wave models and physical models are evaluated
for a better real time performance. The result of this thesis shows that the integration
of a 4-DOF realistic ship model to the system improved the capability of NSTMSS to give
more data to the student officers while making maneuvers. The result also indicates that
the use of waves and ship wakes had taken the simulator to a next level in the environment perception.
23
Al-, Bader S. A. K. "A graphical data model for computer aided engineering of chemical plant." Thesis, University of Leeds, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.504674.
Full text
24
Betack, Charles N. "Graphical analysis of the sensitivites of ATCAL in the FORCEM model." Thesis, Monterey, California. Naval Postgraduate School, 1989. http://hdl.handle.net/10945/27070.
Full text
25
Banham, Stephen R. "TaskMaster: a prototype graphical user interface to a schedule optimization model." Thesis, Monterey, California. Naval Postgraduate School, 1990. http://hdl.handle.net/10945/30673.
Full textAbstract:
Approved for public release, distribution is unlimitedThis thesis investigates the use of current graphical interface techniques to build more effective computer-user interfaces to Operations Research (OR) schedule optimization models. The design is directed at the scheduling decision maker who possesses limited OR experience. The feasibility and validity of building an interface for this kind of user is demonstrated in the development of a prototype graphical user interface called TaskMaster. TaskMaster is designed as the Dialog component of a scheduling Decision Support System (DSS). The underlying scheduling model uses set-partitioning and mixed-integer linear programming to generate optimal schedules. Although the model was originally developed to address a specific problem, inter-deployment scheduling of Navy surface ships, TaskMaster has been designed to be problem-independent, enabling it to address a broad range of scheduling problems with the same general structure. TaskMaster demonstrates the type of interactive, graphical interface that can be developed specifically for non-specialists. It is easy to learn and to use, and yet fully exploits the power of a sophisticated OR scheduling model. The prototype is implemented on a NeXT computer, chosen for its advanced computational power and state-of-the-art graphical interface development tools.
26
Gyftodimos, Elias. "A probabilistic graphical model framework for higher-order term-based representations." Thesis, University of Bristol, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.425088.
Full text
27
Lai, Wai Lok M. Eng Massachusetts Institute of Technology. "A probabilistic graphical model based data compression architecture for Gaussian sources." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/117322.
Full textAbstract:
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.Cataloged from PDF version of thesis.
Includes bibliographical references (pages 107-108).
Data is compressible because of inherent redundancies in the data, mathematically expressed as correlation structures. A data compression algorithm uses the knowledge of these structures to map the original data to a different encoding. The two aspects of data compression, source modeling, ie. using knowledge about the source, and coding, ie. assigning an output sequence of symbols to each output, are not inherently related, but most existing algorithms mix the two and treat the two as one. This work builds on recent research on model-code separation compression architectures to extend this concept into the domain of lossy compression of continuous sources, in particular, Gaussian sources. To our knowledge, this is the first attempt with using with sparse linear coding and discrete-continuous hybrid graphical model decoding for compressing continuous sources. With the flexibility afforded by the modularity of the architecture, we show that the proposed system is free from many inadequacies of existing algorithms, at the same time achieving competitive compression rates. Moreover, the modularity allows for many architectural extensions, with capabilities unimaginable for existing algorithms, including refining of source model after compression, robustness to data corruption, seamless interface with source model parameter learning, and joint homomorphic encryption-compression. This work, meant to be an exploration in a new direction in data compression, is at the intersection of Electrical Engineering and Computer Science, tying together the disciplines of information theory, digital communication, data compression, machine learning, and cryptography.
by Wai Lok Lai.
M. Eng.
28
Shan, Liang. "Joint Gaussian Graphical Model for multi-class and multi-level data." Diss., Virginia Tech, 2016. http://hdl.handle.net/10919/81412.
Full textAbstract:
Gaussian graphical model has been a popular tool to investigate conditional dependency between random variables by estimating sparse precision matrices. The estimated precision matrices could be mapped into networks for visualization. For related but different classes, jointly estimating networks by taking advantage of common structure across classes can help us better estimate conditional dependencies among variables. Furthermore, there may exist multilevel structure among variables; some variables are considered as higher level variables and others are nested in these higher level variables, which are called lower level variables. In this dissertation, we made several contributions to the area of joint estimation of Gaussian graphical models across heterogeneous classes: the first is to propose a joint estimation method for estimating Gaussian graphical models across unbalanced multi-classes, whereas the second considers multilevel variable information during the joint estimation procedure and simultaneously estimates higher level network and lower level network.
For the first project, we consider the problem of jointly estimating Gaussian graphical models across unbalanced multi-class. Most existing methods require equal or similar sample size among classes. However, many real applications do not have similar sample sizes. Hence, in this dissertation, we propose the joint adaptive graphical lasso, a weighted L1 penalized approach, for unbalanced multi-class problems. Our joint adaptive graphical lasso approach combines information across classes so that their common characteristics can be shared during the estimation process. We also introduce regularization into the adaptive term so that the unbalancedness of data is taken into account. Simulation studies show that our approach performs better than existing methods in terms of false positive rate, accuracy, Mathews correlation coefficient, and false discovery rate. We demonstrate the advantage of our approach using liver cancer data set.
For the second one, we propose a method to jointly estimate the multilevel Gaussian graphical models across multiple classes. Currently, methods are still limited to investigate a single level conditional dependency structure when there exists the multilevel structure among variables. Due to the fact that higher level variables may work together to accomplish certain tasks, simultaneously exploring conditional dependency structures among higher level variables and among lower level variables are of our main interest. Given multilevel data from heterogeneous classes, our method assures that common structures in terms of the multilevel conditional dependency are shared during the estimation procedure, yet unique structures for each class are retained as well. Our proposed approach is achieved by first introducing a higher level variable factor within a class, and then common factors across classes. The performance of our approach is evaluated on several simulated networks. We also demonstrate the advantage of our approach using breast cancer patient data.Ph. D.
29
Björnberg, Jakob Erik. "Graphical representations of Ising and Potts models stochastic geometry of the quantum Ising model and the space-time Potts model /." Stockholm : Skolan för teknikvetenskap, Kungliga Tekniska högskolan, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-11267.
Full text
30
Björnberg, Jakob Erik. "Graphical representations of Ising and Potts models : Stochastic geometry of the quantum Ising model and the space-time Potts model." Doctoral thesis, KTH, Matematik (Inst.), 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-11267.
Full textAbstract:
HTML clipboard Statistical physics seeks to explain macroscopic properties of matter in terms of microscopic interactions. Of particular interest is the phenomenon of phase transition: the sudden changes in macroscopic properties as external conditions are varied. Two models in particular are of great interest to mathematicians, namely the Ising model of a magnet and the percolation model of a porous solid. These models in turn are part of the unifying framework of the random-cluster representation, a model for random graphs which was first studied by Fortuin and Kasteleyn in the 1970’s. The random-cluster representation has proved extremely useful in proving important facts about the Ising model and similar models. In this work we study the corresponding graphical framework for two related models. The first model is the transverse field quantum Ising model, an extension of the original Ising model which was introduced by Lieb, Schultz and Mattis in the 1960’s. The second model is the space–time percolation process, which is closely related to the contact model for the spread of disease. In Chapter 2 we define the appropriate space–time random-cluster model and explore a range of useful probabilistic techniques for studying it. The space– time Potts model emerges as a natural generalization of the quantum Ising model. The basic properties of the phase transitions in these models are treated in this chapter, such as the fact that there is at most one unbounded fk-cluster, and the resulting lower bound on the critical value in 
. In Chapter 3 we develop an alternative graphical representation of the quantum Ising model, called the random-parity representation. This representation is based on the random-current representation of the classical Ising model, and allows us to study in much greater detail the phase transition and critical behaviour. A major aim of this chapter is to prove sharpness of the phase transition in the quantum Ising model—a central issue in the theory— and to establish bounds on some critical exponents. We address these issues by using the random-parity representation to establish certain differential inequalities, integration of which gives the results. In Chapter 4 we explore some consequences and possible extensions of the results established in Chapters 2 and 3. For example, we determine the critical point for the quantum Ising model in and in ‘star-like’ geometries.HTML clipboard Statistisk fysik syftar till att förklara ett materials makroskopiska egenskaper i termer av dess mikroskopiska struktur. En särskilt intressant egenskap är är fenomenet fasövergång, det vill säga en plötslig förändring i de makroskopiska egenskaperna när externa förutsättningar varieras. Två modeller är särskilt intressanta för en matematiker, nämligen Ising-modellen av en magnet och perkolationsmodellen av ett poröst material. Dessa två modeller sammanförs av den så-kallade fk-modellen, en slumpgrafsmodell som först studerades av Fortuin och Kasteleyn på 1970-talet. fk-modellen har sedermera visat sig vara extremt användbar för att bevisa viktiga resultat om Ising-modellen och liknande modeller. I den här avhandlingen studeras den motsvarande grafiska strukturen hos två näraliggande modeller. Den första av dessa är den kvantteoretiska Isingmodellen med transverst fält, vilken är en utveckling av den klassiska Isingmodellen och först studerades av Lieb, Schultz och Mattis på 1960-talet. Den andra modellen är rumtid-perkolation, som är nära besläktad med kontaktmodellen av infektionsspridning. I Kapitel 2 definieras rumtid-fk-modellen, och flera probabilistiska verktyg utforskas för att studera dess grundläggande egenskaper. Vi möter rumtid-Potts-modellen, som uppenbarar sig som en naturlig generalisering av den kvantteoretiska Ising-modellen. De viktigaste egenskaperna hos fasövergången i dessa modeller behandlas i detta kapitel, exempelvis det faktum att det i fk-modellen finns högst en obegränsad komponent, samt den undre gräns för det kritiska värdet som detta innebär. I Kapitel 3 utvecklas en alternativ grafisk framställning av den kvantteoretiska Ising-modellen, den så-kallade slumpparitetsframställningen. Denna är baserad på slumpflödesframställningen av den klassiska Ising-modellen, och är ett verktyg som låter oss studera fasövergången och gränsbeteendet mycket närmare. Huvudsyftet med detta kapitel är att bevisa att fasövergången är skarp—en central egenskap—samt att fastslå olikheter för vissa kritiska exponenter. Metoden består i att använda slumpparitetsframställningen för att härleda vissa differentialolikheter, vilka sedan kan integreras för att lägga fast att gränsen är skarp. I Kapitel 4 utforskas några konsekvenser, samt möjliga vidareutvecklingar, av resultaten i de tidigare kapitlen. Exempelvis bestäms det kritiska värdet hos den kvantteoretiska Ising-modellen på
, samt i ‘stjärnliknankde’ geometrier.QC 20100705
31
Björnberg, Jakob Erik. "Graphical representations of Ising and Potts models : stochastic geometry of the quantum Ising model and the space-time Potts model." Thesis, University of Cambridge, 2010. https://www.repository.cam.ac.uk/handle/1810/224774.
Full textAbstract:
Statistical physics seeks to explain macroscopic properties of matter in terms of microscopic interactions. Of particular interest is the phenomenon of phase transition: the sudden changes in macroscopic properties as external conditions are varied. Two models in particular are of great interest to mathematicians, namely the Ising model of a magnet and the percolation model of a porous solid. These models in turn are part of the unifying framework of the random-cluster representation, a model for random graphs which was first studied by Fortuin and Kasteleyn in the 1970's. The random-cluster representation has proved extremely useful in proving important facts about the Ising model and similar models. In this work we study the corresponding graphical framework for two related models. The first model is the transverse field quantum Ising model, an extension of the original Ising model which was introduced by Lieb, Schultz and Mattis in the 1960's. The second model is the space-time percolation process, which is closely related to the contact model for the spread of disease. In Chapter 2 we define the appropriate 'space-time' random-cluster model and explore a range of useful probabilistic techniques for studying it. The space-time Potts model emerges as a natural generalization of the quantum Ising model. The basic properties of the phase transitions in these models are treated in this chapter, such as the fact that there is at most one unbounded fk-cluster, and the resulting lower bound on the critical value in Z. In Chapter 3 we develop an alternative graphical representation of the quantum Ising model, called the random-parity representation. This representation is based on the random-current representation of the classical Ising model, and allows us to study in much greater detail the phase transition and critical behaviour. A major aim of this chapter is to prove sharpness of the phase transition in the quantum Ising model - a central issue in the theory - and to establish bounds on some critical exponents. We address these issues by using the random-parity representation to establish certain differential inequalities, integration of which give the results. In Chapter 4 we explore some consequences and possible extensions of the results established in Chapters 2 and 3. For example, we determine the critical point for the quantum Ising model in Z and in 'star-like' geometries.
32
BENEVIDES, A. B. "A Model-Based graphical editor for supporting the creation, verification and validation of OntoUML conceptual models." Universidade Federal do Espírito Santo, 2010. http://repositorio.ufes.br/handle/10/4211.
Full textAbstract:
Made available in DSpace on 2016-08-29T15:33:10Z (GMT). No. of bitstreams: 1
tese_3332_.pdf: 4500876 bytes, checksum: 55c9192a29c0414e33a92f7897ba7167 (MD5)
Previous issue date: 2010-02-05Essa tese apresenta um editor gráfico baseado em modelos para o suporte à criação, verificação e validação de modelos conceituais e ontologias de domínio em uma linguagem de modelagem filosoficamente e cognitivamente bem-fundada chamada OntoUML. O editor é projetado de forma que, por um lado, ele protege o usuário da complexidade dos princípios ontológicos subjacentes à essa linguagem. Por outro lado, ele reforça esses princípios nos modelos produzidos por prover um mecanismo para verificação formal automática de restrições, daí assegurando que os modelos criados serão sintaticamente corretos. Além disso, avaliar a qualidade de modelos conceituais é um ponto chave para assegurar que modelos conceituais podem ser utilizados efetivamente como uma base para o entendimento, acordo e construção de sistemas de informação. Por essa razão, o editor é também capaz de gerar instâncias de modelos automaticamente por meio da transformação desses modelos em especificações na linguagem, baseada em lógica, chamada Alloy. Como as especificações Alloy geradas incluem os axiomas modais da ontologia fundacional subjacente à OntoUML, chamada Unified Foundational Ontology (UFO), então as instâncias geradas automaticamente vão apresentar um comportamento modal enquanto estiverem sendo classificadas dinamicamente, suportando, assim, a validação das meta-propriedades modais dos tipos fornecidos pela linguagem OntoUML.
33
Aho, P. (Pekka). "Automated state model extraction, testing and change detection through graphical user interface." Doctoral thesis, Oulun yliopisto, 2019. http://urn.fi/urn:isbn:9789526224060.
Full textAbstract:
Abstract Testing is an important part of quality assurance, and the use of agile processes, continuous integration and DevOps is increasing the pressure for automating all aspects of testing. Testing through graphical user interfaces (GUIs) is commonly automated by scripts that are captured or manually created with a script editor, automating the execution of test cases. A major challenge with script-based GUI test automation is the manual effort required for maintaining the scripts when the GUI changes. Model-based testing (MBT) is an approach for automating also the design of test cases. Traditionally, models for MBT are designed manually with a modelling tool, and an MBT tool is used for generating abstract test cases from the model. Then, an adapter is implemented to translate the abstract test cases into concrete test cases that can be executed on system under test (SUT). When the GUI changes, the model has to be updated and the test cases can be generated from the updated model, reducing the maintenance effort. However, designing models and implementing adapters requires effort and specialized expertise. The main research questions of this thesis are 1) how to automatically extract state-based models of software systems with GUI, and 2) how to use the extracted models to automate testing. Our focus is on using dynamic analysis through the GUI during automated exploration of the system, and we concentrate on desktop applications. Our results show that extracting state models through GUI is possible and the models can be used to generate regression test cases, but a more promising approach is to use model comparison on extracted models of consequent system versions to automatically detect changes between the versionsTiivistelmä Testaaminen on tärkeä osa laadun varmistusta. Ketterät kehitysprosessit ja jatkuva integrointi lisäävät tarvetta automatisoida kaikki testauksen osa-alueet. Testaus graafisten käyttöliittymien kautta automatisoidaan yleensä skripteinä, jotka luodaan joko tallentamalla manuaalista testausta tai kirjoittamalla käyttäen skriptieditoria. Tällöin scriptit automatisoivat testitapausten suorittamista. Muutokset graafisessa käyttöliittymässä vaativat scriptien päivittämistä ja scriptien ylläpitoon kuluva työmäärä on iso ongelma. Mallipohjaisessa testauksessa automatisoidaan testien suorittamisen lisäksi myös testitapausten suunnittelu. Perinteisesti mallipohjaisessa testauksessa mallit suunnitellaan manuaalisesti käyttämällä mallinnustyökalua, ja mallista luodaan abstrakteja testitapauksia automaattisesti mallipohjaisen testauksen työkalun avulla. Sen jälkeen implementoidaan adapteri, joka muuttaa abstraktit testitapaukset konkreettisiksi, jotta ne voidaan suorittaa testattavassa järjestelmässä. Kun testattava graafinen käyttöliittymä muuttuu, vain mallia täytyy päivittää ja testitapaukset voidaan luoda automaattisesti uudelleen, vähentäen ylläpitoon käytettävää työmäärää. Mallien suunnittelu ja adapterien implementointi vaatii kuitenkin huomattavan työmäärän ja erikoisosaamista. Tämä väitöskirja tutkii 1) voidaanko tilamalleja luoda automaattisesti järjestelmistä, joissa on graafinen käyttöliittymä, ja 2) voidaanko automaattisesti luotuja tilamalleja käyttää testauksen automatisointiin. Tutkimus keskittyy työpöytäsovelluksiin ja dynaamisen analyysin käyttämiseen graafisen käyttöliittymän kautta järjestelmän automatisoidun läpikäynnin aikana. Tutkimustulokset osoittavat, että tilamallien automaattinen luominen graafisen käyttöliittymän kautta on mahdollista, ja malleja voidaan käyttää testitapausten generointiin regressiotestauksessa. Lupaavampi lähestymistapa on kuitenkin vertailla malleja, jotka on luotu järjestelmän peräkkäisistä versioista, ja havaita versioiden väliset muutokset automaattisesti
34
Zhang, Yafei. "Variable screening and graphical modeling for ultra-high dimensional longitudinal data." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/101662.
Full textAbstract:
Ultrahigh-dimensional variable selection is of great importance in the statistical research. And independence screening is a powerful tool to select important variable when there are massive variables. Some commonly used independence screening procedures are based on single replicate data and are not applicable to longitudinal data. This motivates us to propose a new Sure Independence Screening (SIS) procedure to bring the dimension from ultra-high down to a relatively large scale which is similar to or smaller than the sample size. In chapter 2, we provide two types of SIS, and their iterative extensions (iterative SIS) to enhance the finite sample performance. An upper bound on the number of variables to be included is derived and assumptions are given under which sure screening is applicable. The proposed procedures are assessed by simulations and an application of them to a study on systemic lupus erythematosus illustrates the practical use of these procedures. After the variables screening process, we then explore the relationship among the variables. Graphical models are commonly used to explore the association network for a set of variables, which could be genes or other objects under study. However, graphical modes currently used are only designed for single replicate data, rather than longitudinal data. In chapter 3, we propose a penalized likelihood approach to identify the edges in a conditional independence graph for longitudinal data. We used pairwise coordinate descent combined with second order cone programming to optimize the penalized likelihood and estimate the parameters. Furthermore, we extended the nodewise regression method the for longitudinal data case. Simulation and real data analysis exhibit the competitive performance of the penalized likelihood method.Doctor of Philosophy
35
Sudarshan, Chavva. "An Interactive Graphical User Interface Generator in the Client-Server Computational Model." TopSCHOLAR®, 1995. http://digitalcommons.wku.edu/theses/887.
Full textAbstract:
Providing a user friendly and sophisticated user interface is a time-consuming and difficult task. On the other hand, the success of a software system is directly related to the quality of the user interface that it provides. In this thesis, a technique for the development of an automatic user interface is described and implemented; that is, we provide a tool that can be used to develop user interfaces on a platform known as the X-Window System. The hallmark of this tool is its ability to aid in developing reasonably sophisticated Graphical User Interfaces (GUI) interactively in relatively short time. The burden of programming is completely eliminated for the developer of the GUI, enabling him/her to concentrate on the details of the application for which the GUI is being developed, thus boosting his productivity. An attempt has been made to accommodate many features for the development of User Interfaces and provision has been made so that the implementation can be extended to include additional features easily. Buttons and cascade menus can be created, and functions can be associated with them just by a click of the mouse button.
36
Karzand, Mina. "Theoretical study of two prediction-centric problems : graphical model learning and recommendations." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/114030.
Full textAbstract:
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 177-184).
Motivated by prediction-centric learning problems, two problems are discussed in this thesis. PART I. Learning a tree-structured Ising model: We study the problem of learning a tree Ising model from samples such that subsequent predictions based on partial observations are accurate. Virtually all previous work on graphical model learning has focused on recovering the true underlying graph. We dene a distance ("small set TV" or ssTV) between distributions P and Q by taking the maximum, over all subsets S of a given size, of the total variation between the marginals of P and Q on S; this distance captures the accuracy of the prediction task of interest. We derive non-asymptotic bounds on the number of samples needed to get a distribution (from the same class) with small ssTV relative to the one generating the samples. An implication is that far fewer samples are needed for accurate predictions than for recovering the underlying tree. PART II. Optimal online algorithms for a latent variable model of recommendation systems: We consider an online model for recommendation systems, with each user being recommended an item at each time-step and providing 'like' or 'dislike' feedback. The user preferences are specified via a latent variable model: both users and items are clustered into types. The model captures structure in both the item and user spaces, and our focus is on simultaneous use of both structures. In the case when the type preference matrix is randomly generated, we provide a sharp analysis of the best possible regret obtainable by any algorithm.
by Mina Karzand.
Ph. D.
37
Sadeghi, Kayvan. "Graphical representation of independence structures." Thesis, University of Oxford, 2012. http://ora.ox.ac.uk/objects/uuid:86ff6155-a6b9-48f9-9dac-1ab791748072.
Full textAbstract:
In this thesis we describe subclasses of a class of graphs with three types of edges, called loopless mixed graphs (LMGs). The class of LMGs contains almost all known classes of graphs used in the literature of graphical Markov models. We focus in particular on the subclass of ribbonless graphs (RGs), which as special cases include undirected graphs, bidirected graphs, and directed acyclic graphs, as well as ancestral graphs and summary graphs. We define a unifying interpretation of independence structure for LMGs and pairwise and global Markov properties for RGs, discuss their maximality, and, in particular, prove the equivalence of pairwise and global Markov properties for graphoids defined over the nodes of RGs. Three subclasses of LMGs (MC, summary, and ancestral graphs) capture the modified independence model after marginalisation over unobserved variables and conditioning on selection variables of variables satisfying independence restrictions represented by a directed acyclic graph (DAG). We derive algorithms to generate these graphs from a given DAG or from a graph of a specific subclass, and we study the relationships between these classes of graphs. Finally, a manual and codes are provided that explain methods and functions in R for implementing and generating various graphs studied in this thesis.
38
Zhao, Haitao. "Learning Genetic Networks Using Gaussian Graphical Model and Large-Scale Gene Expression Data." University of Akron / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=akron1595682639738664.
Full text
39
Angelchev, Shiryaev Artem, and Johan Karlsson. "Estimating Dependence Structures with Gaussian Graphical Models : A Simulation Study in R." Thesis, Umeå universitet, Statistik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-184925.
Full textAbstract:
Graphical models are powerful tools when estimating complex dependence structures among large sets of data. This thesis restricts the scope to undirected Gaussian graphical models. An initial predefined sparse precision matrix was specified to generate multivariate normally distributed data. Utilizing the generated data, a simulation study was conducted reviewing accuracy, sensitivity and specificity of the estimated precision matrix. The graphical LASSO was applied using four different packages available in R with seven selection criteria's for estimating the tuning parameter. The findings are mostly in line with previous research. The graphical LASSO is generally faster and feasible in high dimensions, in contrast to stepwise model selection. A portion of the selection methods for estimating the optimal tuning parameter obtained the true network structure. The results provide an estimate of how well each model obtains the true, predefined dependence structure as featured in our simulation. As the simulated data used in this thesis is merely an approximation of real-world data, one should not take the results as the only aspect of consideration when choosing a model.
40
Kontos, Kevin. "Gaussian graphical model selection for gene regulatory network reverse engineering and function prediction." Doctoral thesis, Universite Libre de Bruxelles, 2009. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/210301.
Full textAbstract:
One of the most important and challenging ``knowledge extraction' tasks in bioinformatics is the reverse engineering of gene regulatory networks (GRNs) from DNA microarray gene expression data. Indeed, as a result of the development of high-throughput data-collection techniques, biology is experiencing a data flood phenomenon that pushes biologists toward a new view of biology--systems biology--that aims at system-level understanding of biological systems.Unfortunately, even for small model organisms such as the yeast Saccharomyces cerevisiae, the number p of genes is much larger than the number n of expression data samples. The dimensionality issue induced by this ``small n, large p' data setting renders standard statistical learning methods inadequate. Restricting the complexity of the models enables to deal with this serious impediment. Indeed, by introducing (a priori undesirable) bias in the model selection procedure, one reduces the variance of the selected model thereby increasing its accuracy.
Gaussian graphical models (GGMs) have proven to be a very powerful formalism to infer GRNs from expression data. Standard GGM selection techniques can unfortunately not be used in the ``small n, large p' data setting. One way to overcome this issue is to resort to regularization. In particular, shrinkage estimators of the covariance matrix--required to infer GGMs--have proven to be very effective. Our first contribution consists in a new shrinkage estimator that improves upon existing ones through the use of a Monte Carlo (parametric bootstrap) procedure.
Another approach to GGM selection in the ``small n, large p' data setting consists in reverse engineering limited-order partial correlation graphs (q-partial correlation graphs) to approximate GGMs. Our second contribution consists in an inference algorithm, the q-nested procedure, that builds a sequence of nested q-partial correlation graphs to take advantage of the smaller order graphs' topology to infer higher order graphs. This allows us to significantly speed up the inference of such graphs and to avoid problems related to multiple testing. Consequently, we are able to consider higher order graphs, thereby increasing the accuracy of the inferred graphs.
Another important challenge in bioinformatics is the prediction of gene function. An example of such a prediction task is the identification of genes that are targets of the nitrogen catabolite repression (NCR) selection mechanism in the yeast Saccharomyces cerevisiae. The study of model organisms such as Saccharomyces cerevisiae is indispensable for the understanding of more complex organisms. Our third contribution consists in extending the standard two-class classification approach by enriching the set of variables and comparing several feature selection techniques and classification algorithms.
Finally, our fourth contribution formulates the prediction of NCR target genes as a network inference task. We use GGM selection to infer multivariate dependencies between genes, and, starting from a set of genes known to be sensitive to NCR, we classify the remaining genes. We hence avoid problems related to the choice of a negative training set and take advantage of the robustness of GGM selection techniques in the ``small n, large p' data setting.
Doctorat en Sciences
info:eu-repo/semantics/nonPublished
41
Vinyes, Marina. "Convex matrix sparsity for demixing with an application to graphical model structure estimation." Thesis, Paris Est, 2018. http://www.theses.fr/2018PESC1130/document.
Full textAbstract:
En apprentissage automatique on a pour but d'apprendre un modèle, à partir de données, qui soit capable de faire des prédictions sur des nouvelles données (pas explorées auparavant). Pour obtenir un modèle qui puisse se généraliser sur les nouvelles données, et éviter le sur-apprentissage, nous devons restreindre le modèle. Ces restrictions sont généralement une connaissance a priori de la structure du modèle. Les premières approches considérées dans la littérature sont la régularisation de Tikhonov et plus tard le Lasso pour induire de la parcimonie dans la solution. La parcimonie fait partie d'un concept fondamental en apprentissage automatique. Les modèles parcimonieux sont attrayants car ils offrent plus d'interprétabilité et une meilleure généralisation (en évitant le sur-apprentissage) en induisant un nombre réduit de paramètres dans le modèle. Au-delà de la parcimonie générale et dans de nombreux cas, les modèles sont structurellement contraints et ont une représentation simple de certains éléments fondamentaux, comme par exemple une collection de vecteurs, matrices ou tenseurs spécifiques. Ces éléments fondamentaux sont appelés atomes. Dans ce contexte, les normes atomiques fournissent un cadre général pour estimer ce type de modèles. périodes de modèles. Le but de cette thèse est d'utiliser le cadre de parcimonie convexe fourni par les normes atomiques pour étudier une forme de parcimonie matricielle. Tout d'abord, nous développons un algorithme efficace basé sur les méthodes de Frank-Wolfe et qui est particulièrement adapté pour résoudre des problèmes convexes régularisés par une norme atomique. Nous nous concentrons ensuite sur l'estimation de la structure des modèles graphiques gaussiens, où la structure du modèle est encodée dans la matrice de précision et nous étudions le cas avec des variables manquantes. Nous proposons une formulation convexe avec une approche algorithmique et fournissons un résultat théorique qui énonce les conditions nécessaires pour récupérer la structure souhaitée. Enfin, nous considérons le problème de démixage d'un signal en deux composantes ou plus via la minimisation d’une somme de normes ou de jauges, encodant chacune la structure a priori des composants à récupérer. En particulier, nous fournissons une garantie de récupération exacte dans le cadre sans bruit, basée sur des mesures d'incohérenceThe goal of machine learning is to learn a model from some data that will make accurate predictions on data that it has not seen before. In order to obtain a model that will generalize on new data, and avoid overfitting, we need to restrain the model. These restrictions are usually some a priori knowledge of the structure of the model. First considered approaches included a regularization, first ridge regression and later Lasso regularization for inducing sparsity in the solution. Sparsity, also known as parsimony, has emerged as a fundamental concept in machine learning. Parsimonious models are appealing since they provide more interpretability and better generalization (avoid overfitting) through the reduced number of parameters. Beyond general sparsity and in many cases, models are constrained structurally so they have a simple representation in terms of some fundamental elements, consisting for example of a collection of specific vectors, matrices or tensors. These fundamental elements are called atoms. In this context, atomic norms provide a general framework for estimating these sorts of models. The goal of this thesis is to use the framework of convex sparsity provided by atomic norms to study a form of matrix sparsity. First, we develop an efficient algorithm based on Frank-Wolfe methods that is particularly adapted to solve problems with an atomic norm regularization. Then, we focus on the structure estimation of Gaussian graphical models, where the structure of the graph is encoded in the precision matrix and study the case with unobserved variables. We propose a convex formulation with an algorithmic approach and provide a theoretical result that states necessary conditions for recovering the desired structure. Finally, we consider the problem of signal demixing into two or more components via the minimization of a sum of norms or gauges, encoding each a structural prior on the corresponding components to recover. In particular, we provide general exact recovery guarantees in the noiseless setting based on incoherence measures
42
Sadam, Chalapathirao Kishan. "The design of graphical output interface for the runway exit design interactive model." Master's thesis, Virginia Tech, 1990. http://hdl.handle.net/10919/45687.
Full text
43
Toy, David J. "Comparison of graphical terrain resolutions by scenario for the Janus(A) combat model." Thesis, Monterey, Calif. : Naval Postgraduate School, 1992. http://handle.dtic.mil/100.2/ADA247788.
Full textAbstract:
Thesis (M.S. in Information Systems)--Naval Postgraduate School, March 1992.Thesis Advisors: Barr, Donald R. ; Bundy, Dennis D. "March 1992." Includes bibliographical references (p. 31). Also available in print.
44
Ramani, Shiva Shankar. "Graphical Probabilistic Switching Model: Inference and Characterization for Power Dissipation in VLSI Circuits." [Tampa, Fla.] : University of South Florida, 2004. http://purl.fcla.edu/fcla/etd/SFE0000497.
Full text
45
Miniukovich, Aliaksei. "Computational Aesthetics in HCI: Towards a Predictive Model of Graphical User Interface Aesthetics." Doctoral thesis, Università degli studi di Trento, 2016. https://hdl.handle.net/11572/368110.
Full textAbstract:
This thesis describes the development and validation of a predictive model of graphical user interface (GUI) aesthetics. The development was informed by the processing-fluency theory of aesthetic pleasure and involved outlining several visual dimensions of GUI designs, which could affect aesthetics impression. Each of the dimensions was grounded in theory and represents a unique visual aspect of GUI design. The resulting model automatically evaluates the design dimensions and combines them in an estimate of the average impression that GUI appearance would make on the user population. The model was validated in a number of user studies proving high validity and reliability. The model outputs an aesthetics score ofuser impression and could inform the creation of more beautiful GUIs by highlighting which of the design dimensions could be improved. The thesis describes the studies that validated the model on several types of GUIs and demonstrated a potential application of the model in future research and practice.
46
Martinelli, Joseph A. "An X11 graphical interface for the REpresentation and MAintenance of Process Knowledge (REMAP) model /." Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from the National Technical Information Service, 1993. http://handle.dtic.mil/100.2/ADA273169.
Full text
47
Wyant, Marvin Abram. "Design and implementation of a prototype graphical user interface for a model management system." Thesis, Monterey, California : Naval Postgraduate School, 1988. http://hdl.handle.net/10945/23010.
Full textAbstract:
The purpose of this thesis is to design and implement a prototype graphical user interface for a structured model management system. The program is written for an IBM PC using Lattice-C, the Halo graphics package, and the ORACLE DBMS. Design and Implementation issues are discussed and evaluated. Future enhancements to the program and a recommendation as to the disposition of the prototype are also included. A brief explanation of structured modeling is presented. An example problem is used to illustrate the various model representation of structured modeling from a database representation. The results of this thesis show that the prototype design methodology is an excellent supplement to the traditional life-cycle design methodology. The implications of this observation are discussed in relationship to the graphical user interface program. Keywords: Structured models; Model management system; MMS; User interface; Prototype; Graphics; System design; Database. (jes)
48
Martinelli, Joseph Anthony. "An X11 graphical interface for the REpresentation and MAintenance of Process Knowledge (REMAP) model." Thesis, Monterey, California. Naval Postgraduate School, 1993. http://hdl.handle.net/10945/39975.
Full textAbstract:
Approved for public release; distribution is unlimited.The REpresentation and MAintenance of Process knowledge (REMAP) model provides support to various stakeholders involved in software projects by capturing the history of design decisions. This knowledge can assist the Department of Defense (DoD) in driving down the development and maintenance costs of large scale software systems. It is extremely important to have user friendly mechanisms to aid in the use of the REMAP model. This thesis implements a graphical user interface (GUI) under X11 Windows using the Andrew Toolkit. This implementation facilitates the instantiation, incremental modification, and ad-hoc querying of REMAP model primitives.
49
Li, Nan. "Maximum Likelihood Identification of an Information Matrix Under Constraints in a Corresponding Graphical Model." Digital WPI, 2017. https://digitalcommons.wpi.edu/etd-theses/128.
Full textAbstract:
We address the problem of identifying the neighborhood structure of an undirected graph, whose nodes are labeled with the elements of a multivariate normal (MVN) random vector. A semi-definite program is given for estimating the information matrix under arbitrary constraints on its elements. More importantly, a closed-form expression is given for the maximum likelihood (ML) estimator of the information matrix, under the constraint that the information matrix has pre-specified elements in a given pattern (e.g., in a principal submatrix). The results apply to the identification of dependency labels in a graphical model with neighborhood constraints. This neighborhood structure excludes nodes which are conditionally independent of a given node and the graph is determined by the non- zero elements in the information matrix for the random vector. A cross-validation principle is given for determining whether the constrained information matrix returned from this procedure is an acceptable model for the information matrix, and as a consequence for the neighborhood structure of the Markov Random Field (MRF) that is identified with the MVN random vector.
50
Obembe, Olufunmilayo. "Development of a probabilistic graphical structure from a model of mental health clinical expertise." Thesis, Aston University, 2013. http://publications.aston.ac.uk/19432/.
Full textAbstract:
This thesis explores the process of developing a principled approach for translating a model of mental-health risk expertise into a probabilistic graphical structure. Probabilistic graphical structures can be a combination of graph and probability theory that provide numerous advantages when it comes to the representation of domains involving uncertainty, domains such as the mental health domain. In this thesis the advantages that probabilistic graphical structures offer in representing such domains is built on. The Galatean Risk Screening Tool (GRiST) is a psychological model for mental health risk assessment based on fuzzy sets. In this thesis the knowledge encapsulated in the psychological model was used to develop the structure of the probability graph by exploiting the semantics of the clinical expertise. This thesis describes how a chain graph can be developed from the psychological model to provide a probabilistic evaluation of risk that complements the one generated by GRiST’s clinical expertise by the decomposing of the GRiST knowledge structure in component parts, which were in turned mapped into equivalent probabilistic graphical structures such as Bayesian Belief Nets and Markov Random Fields to produce a composite chain graph that provides a probabilistic classification of risk expertise to complement the expert clinical judgements