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Relevant bibliographies by topics / Probabilistic Graphical Model

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Academic literature on the topic 'Probabilistic Graphical Model'

Author: Grafiati

Published: 4 June 2021

Last updated: 11 March 2023

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Journal articles on the topic "Probabilistic Graphical Model"

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Gouiouez, Mounir. "Probabilistic Graphical Model based on BablNet for Arabic Text Classification." Journal of Advanced Research in Dynamical and Control Systems 12, SP7 (July 25, 2020): 1241–50. http://dx.doi.org/10.5373/jardcs/v12sp7/20202224.

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Höhna, Sebastian, Tracy A. Heath, Bastien Boussau, Michael J. Landis, Fredrik Ronquist, and John P. Huelsenbeck. "Probabilistic Graphical Model Representation in Phylogenetics." Systematic Biology 63, no. 5 (June 20, 2014): 753–71. http://dx.doi.org/10.1093/sysbio/syu039.

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Javidian, Mohammad Ali, Zhiyu Wang, Linyuan Lu, and Marco Valtorta. "On a hypergraph probabilistic graphical model." Annals of Mathematics and Artificial Intelligence 88, no. 9 (July 10, 2020): 1003–33. http://dx.doi.org/10.1007/s10472-020-09701-7.

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Denev, Alexander, Adrien Papaioannou, and Orazio Angelini. "A probabilistic graphical models approach to model interconnectedness." International Journal of Risk Assessment and Management 23, no. 2 (2020): 119. http://dx.doi.org/10.1504/ijram.2020.10028855.

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Denev, Alexander, Adrien Papaioannou, and Orazio Angelini. "A probabilistic graphical models approach to model interconnectedness." International Journal of Risk Assessment and Management 23, no. 2 (2020): 119. http://dx.doi.org/10.1504/ijram.2020.106963.

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Ahn, Gil Seung, and Sun Hur. "Probabilistic Graphical Model for Transaction Data Analysis." Journal of Korean Institute of Industrial Engineers 42, no. 4 (August 15, 2016): 249–55. http://dx.doi.org/10.7232/jkiie.2016.42.4.249.

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Wan, Jiang, and Nicholas Zabaras. "A probabilistic graphical model based stochastic input model construction." Journal of Computational Physics 272 (September 2014): 664–85. http://dx.doi.org/10.1016/j.jcp.2014.05.002.

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KRAUSE, PAUL J. "Learning probabilistic networks." Knowledge Engineering Review 13, no. 4 (February 1999): 321–51. http://dx.doi.org/10.1017/s0269888998004019.

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A probabilistic network is a graphical model that encodes probabilistic relationships between variables of interest. Such a model records qualitative influences between variables in addition to the numerical parameters of the probability distribution. As such it provides an ideal form for combining prior knowledge, which might be limited solely to experience of the influences between some of the variables of interest, and data. In this paper, we first show how data can be used to revise initial estimates of the parameters of a model. We then progress to showing how the structure of the model can be revised as data is obtained. Techniques for learning with incomplete data are also covered. In order to make the paper as self contained as possible, we start with an introduction to probability theory and probabilistic graphical models. The paper concludes with a short discussion on how these techniques can be applied to the problem of learning causal relationships between variables in a domain of interest.

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Murray, Richard F. "A probabilistic graphical model of lightness and lighting." Journal of Vision 19, no. 10 (September 6, 2019): 298a. http://dx.doi.org/10.1167/19.10.298a.

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Zhang, Mingjie, and Baosheng Kang. "Visual Tracking Algorithm Based on Probabilistic Graphical Model." International Journal of Signal Processing, Image Processing and Pattern Recognition 8, no. 9 (September 30, 2015): 157–66. http://dx.doi.org/10.14257/ijsip.2015.8.9.16.

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Dissertations / Theses on the topic "Probabilistic Graphical Model"

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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.

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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.

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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.

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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.

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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.

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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/.

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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

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Yoo, Keunyoung. "Probabilistic SEM : an augmentation to classical Structural equation modelling." Diss., University of Pretoria, 2018. http://hdl.handle.net/2263/66521.

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Structural equation modelling (SEM) is carried out with the aim of testing hypotheses on the model of the researcher in a quantitative way, using the sampled data. Although SEM has developed in many aspects over the past few decades, there are still numerous advances which can make SEM an even more powerful technique. We propose representing the nal theoretical SEM by a Bayesian Network (BN), which we would like to call a Probabilistic Structural Equation Model (PSEM). With the PSEM, we can take things a step further and conduct inference by explicitly entering evidence into the network and performing di erent types of inferences. Because the direction of the inference is not an issue, various scenarios can be simulated using the BN. The augmentation of SEM with BN provides signi cant contributions to the eld. Firstly, structural learning can mine data for additional causal information which is not necessarily clear when hypothesising causality from theory. Secondly, the inference ability of the BN provides not only insight as mentioned before, but acts as an interactive tool as the `what-if' analysis is dynamic.
Mini Dissertation (MCom)--University of Pretoria, 2018.
Statistics
MCom
Unrestricted

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Malings, Carl Albert. "Optimal Sensor Placement for Infrastructure System Monitoring using Probabilistic Graphical Models and Value of Information." Research Showcase @ CMU, 2017. http://repository.cmu.edu/dissertations/869.

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Civil infrastructure systems form the backbone of modern civilization, providing the basic services that allow society to function. Effective management of these systems requires decision-making about the allocation of limited resources to maintain and repair infrastructure components and to replace failed or obsolete components. Making informed decisions requires an understanding of the state of the system; such an understanding can be achieved through a computational or conceptual system model combined with information gathered on the system via inspections or sensors. Gathering of this information, referred to generally as sensing, should be optimized to best support the decision-making and system management processes, in order to reduce long-term operational costs and improve infrastructure performance. In this work, an approach to optimal sensing in infrastructure systems is developed by combining probabilistic graphical models of infrastructure system behavior with the value of information (VoI) metric, which quantifies the utility of information gathering efforts (referred to generally as sensor placements) in supporting decision-making in uncertain systems. Computational methods are presented for the efficient evaluation and optimization of the VoI metric based on the probabilistic model structure. Various case studies on the application of this approach to managing infrastructure systems are presented, illustrating the flexibility of the basic method as well as various special cases for its practical implementation. Three main contributions are presented in this work. First, while the computational complexity of the VoI metric generally grows exponentially with the number of components, growth can be greatly reduced in systems with certain topologies (designated as cumulative topologies). Following from this, an efficient approach to VoI computation based on a cumulative topology and Gaussian random field model is developed and presented. Second, in systems with non-cumulative topologies, approximate techniques may be used to evaluate the VoI metric. This work presents extensive investigations of such systems and draws some general conclusions about the behavior of this metric. Third, this work presents several complete application cases for probabilistic modeling techniques and the VoI metric in supporting infrastructure system management. Case studies are presented in structural health monitoring, seismic risk mitigation, and extreme temperature response in urban areas. Other minor contributions included in this work are theoretical and empirical comparisons of the VoI with other sensor placement metrics and an extension of the developed sensor placement method to systems that evolve in time. Overall, this work illustrates how probabilistic graphical models and the VoI metric can allow for efficient sensor placement optimization to support infrastructure system management. Areas of future work to expand on the results presented here include the development of approximate, heuristic methods to support efficient sensor placement in non-cumulative system topologies, as well as further validation of the efficient sensing optimization approaches used in this work.

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Piao, Dongzhen. "Speeding Up Gibbs Sampling in Probabilistic Optical Flow." Research Showcase @ CMU, 2014. http://repository.cmu.edu/dissertations/481.

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In today’s machine learning research, probabilistic graphical models are used extensively to model complicated systems with uncertainty, to help understanding of the problems, and to help inference and predict unknown events. For inference tasks, exact inference methods such as junction tree algorithms exist, but they suffer from exponential growth of cluster size and thus is not able to handle large and highly connected graphs. Approximate inference methods do not try to find exact probabilities, but rather give results that improve as algorithm runs. Gibbs sampling, as one of the approximate inference methods, has gained lots of traction and is used extensively in inference tasks, due to its ease of understanding and implementation. However, as problem size grows, even the faster algorithm needs a speed boost to meet application requirement. The number of variables in an application graphical model can range from tens of thousands to billions, depending on problem domain. The original sequential Gibbs sampling may not return satisfactory result in limited time. Thus, in this thesis, we investigate in ways to speed up Gibbs sampling. We will study ways to do better initialization, blocking variables to be sampled together, as well as using simulated annealing. These are the methods that modifies the algorithm itself. We will also investigate in ways to parallelize the algorithm. An algorithm is parallelizable if some steps do not depend on other steps, and we will find out such dependency in Gibbs sampling. We will discuss how the choice of different hardware and software architecture will affect the parallelization result. We will use optical flow problem as an example to demonstrate the various speed up methods we investigated. An optical flow method tries to find out the movements of small image patches between two images in a temporal sequence. We demonstrate how we can model it using probabilistic graphical model, and solve it using Gibbs sampling. The result of using sequential Gibbs sampling is demonstrated, with comparisons from using various speed up methods and other optical flow methods.

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Kausler, Bernhard [Verfasser], and Fred A. [Akademischer Betreuer] Hamprecht. "Tracking-by-Assignment as a Probabilistic Graphical Model with Applications in Developmental Biology / Bernhard Kausler ; Betreuer: Fred A. Hamprecht." Heidelberg : Universitätsbibliothek Heidelberg, 2013. http://d-nb.info/1177381079/34.

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Wang, Chao. "Exploiting non-redundant local patterns and probabilistic models for analyzing structured and semi-structured data." Columbus, Ohio : Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1199284713.

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Books on the topic "Probabilistic Graphical Model"

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Portinale, Luigi. Modeling and analysis of dependable systems: A probabilistic graphical model perspective. New Jersey: World Scientific, 2015.

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Sucar, Luis Enrique. Probabilistic Graphical Models. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-61943-5.

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van der Gaag, Linda C., and Ad J. Feelders, eds. Probabilistic Graphical Models. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11433-0.

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Sucar, Luis Enrique. Probabilistic Graphical Models. London: Springer London, 2015. http://dx.doi.org/10.1007/978-1-4471-6699-3.

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1955-, Lucas Peter, Gámez José A, and Salmerón Antonio, eds. Advances in probabilistic graphical models. Berlin: Springer, 2007.

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Lucas, Peter, José A. Gámez, and Antonio Salmerón, eds. Advances in Probabilistic Graphical Models. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-68996-6.

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Nir, Friedman, ed. Probabilistic graphical models: Principles and techniques. Cambridge, MA: MIT Press, 2010.

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Dechter, Rina. Reasoning with Probabilistic and Deterministic Graphical Models. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-031-01583-0.

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Dechter, Rina. Reasoning with Probabilistic and Deterministic Graphical Models. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-031-01566-3.

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Dechter, Rina. Reasoning with probabilistic and deterministic graphical models: Exact algorithms. San Rafael, California]: Morgan & Claypool Publishers, 2013.

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Book chapters on the topic "Probabilistic Graphical Model"

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Polani, Daniel. "Probabilistic Graphical Model." In Encyclopedia of Systems Biology, 1748. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_1553.

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Kraisangka, Jidapa, and Marek J. Druzdzel. "Discrete Bayesian Network Interpretation of the Cox’s Proportional Hazards Model." In Probabilistic Graphical Models, 238–53. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11433-0_16.

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Zhou, Yun, Norman Fenton, and Martin Neil. "An Extended MPL-C Model for Bayesian Network Parameter Learning with Exterior Constraints." In Probabilistic Graphical Models, 581–96. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11433-0_38.

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Pedemonte, Stefano, Alexandre Bousse, Brian F. Hutton, Simon Arridge, and Sebastien Ourselin. "Probabilistic Graphical Model of SPECT/MRI." In Machine Learning in Medical Imaging, 167–74. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24319-6_21.

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Wang, Jing, Jinglin Zhou, and Xiaolu Chen. "Probabilistic Graphical Model for Continuous Variables." In Intelligent Control and Learning Systems, 251–65. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8044-1_14.

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AbstractMost of the sampled data in complex industrial processes are sequential in time. Therefore, the traditional BN learning mechanisms have limitations on the value of probability and cannot be applied to the time series. The model established in Chap. 10.1007/978-981-16-8044-1_13 is a graphical model similar to a Bayesian network, but its parameter learning method can only handle the discrete variables. This chapter aims at the probabilistic graphical model directly for the continuous process variables, which avoids the assumption of discrete or Gaussian distributions.

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Tanaka, Kazuyuki. "Review of Sublinear Modeling in Probabilistic Graphical Models by Statistical Mechanical Informatics and Statistical Machine Learning Theory." In Sublinear Computation Paradigm, 165–275. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-4095-7_10.

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AbstractWe review sublinear modeling in probabilistic graphical models by statistical mechanical informatics and statistical machine learning theory. Our statistical mechanical informatics schemes are based on advanced mean-field methods including loopy belief propagations. This chapter explores how phase transitions appear in loopy belief propagations for prior probabilistic graphical models. The frameworks are mainly explained for loopy belief propagations in the Ising model which is one of the elementary versions of probabilistic graphical models. We also expand the schemes to quantum statistical machine learning theory. Our framework can provide us with sublinear modeling based on the momentum space renormalization group methods.

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Bermejo, Iñigo, Francisco Javier Díez, Paul Govaerts, and Bart Vaerenberg. "A Probabilistic Graphical Model for Tuning Cochlear Implants." In Artificial Intelligence in Medicine, 150–55. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38326-7_23.

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Diaz, Elva, Eunice Ponce-de-Leon, Pedro Larrañaga, and Concha Bielza. "Probabilistic Graphical Markov Model Learning: An Adaptive Strategy." In MICAI 2009: Advances in Artificial Intelligence, 225–36. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-05258-3_20.

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Zhao, Feng, Jian Peng, Joe DeBartolo, Karl F. Freed, Tobin R. Sosnick, and Jinbo Xu. "A Probabilistic Graphical Model for Ab Initio Folding." In Lecture Notes in Computer Science, 59–73. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02008-7_5.

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Paquet, Hugo. "Bayesian strategies: probabilistic programs as generalised graphical models." In Programming Languages and Systems, 519–47. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72019-3_19.

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AbstractWe introduceBayesian strategies, a new interpretation of probabilistic programs in game semantics. This interpretation can be seen as a refinement of Bayesian networks.Bayesian strategies are based on a new form ofevent structure, with two causal dependency relations respectively modelling control flow and data flow. This gives a graphical representation for probabilistic programs which resembles the concrete representations used in modern implementations of probabilistic programming.From a theoretical viewpoint, Bayesian strategies provide a rich setting for denotational semantics. To demonstrate this we give a model for a general higher-order programming language with recursion, conditional statements, and primitives for sampling from continuous distributions and trace re-weighting. This is significant because Bayesian networks do not easily support higher-order functions or conditionals.

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Conference papers on the topic "Probabilistic Graphical Model"

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Yeang, Chen-Hsiang. "A Probabilistic Graphical Model of Quantum Systems." In 2010 International Conference on Machine Learning and Applications (ICMLA). IEEE, 2010. http://dx.doi.org/10.1109/icmla.2010.30.

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Peleato, Borja, Rajiv Agarwal, and John Cioffi. "Probabilistic graphical model for flash memory programming." In 2012 IEEE Statistical Signal Processing Workshop (SSP). IEEE, 2012. http://dx.doi.org/10.1109/ssp.2012.6319823.

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Dake Zhou, Yong Xu, Jingwei Huang, and Xin Yang. "Fragments-based object tracking using probabilistic graphical model." In 2016 IEEE Chinese Guidance, Navigation and Control Conference (CGNCC). IEEE, 2016. http://dx.doi.org/10.1109/cgncc.2016.7828908.

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Sekharan, Chandra N. "A probabilistic graphical model for learning as search." In 2017 IEEE 7th Annual Computing and Communication Workshop and Conference (CCWC). IEEE, 2017. http://dx.doi.org/10.1109/ccwc.2017.7868379.

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Fang, Meiyuan, and Jiangtao Wen. "Probabilistic Graphical Model Based Fast HEVC Inter Prediction." In 2017 Data Compression Conference (DCC). IEEE, 2017. http://dx.doi.org/10.1109/dcc.2017.94.

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Dong-jin Fan and Ju-fu Feng. "A fingerprint matching algorithm using probabilistic graphical model." In 2009 16th IEEE International Conference on Image Processing ICIP 2009. IEEE, 2009. http://dx.doi.org/10.1109/icip.2009.5414168.

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Fang, Meiyuan, Jiangtao Wen, and Yuxing Han. "Probabilistic graphical model based fast HEVC inter prediction." In 2017 IEEE International Conference on Image Processing (ICIP). IEEE, 2017. http://dx.doi.org/10.1109/icip.2017.8296234.

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Smith, David, Sara Rouhani, and Vibhav Gogate. "Order Statistics for Probabilistic Graphical Models." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/645.

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We consider the problem of computing r-th order statistics, namely finding an assignment having rank r in a probabilistic graphical model. We show that the problem is NP-hard even when the graphical model has no edges (zero-treewidth models) via a reduction from the partition problem. We use this reduction, specifically a pseudo-polynomial time algorithm for number partitioning to yield a pseudo-polynomial time approximation algorithm for solving the r-th order statistics problem in zero- treewidth models. We then extend this algorithm to arbitrary graphical models by generalizing it to tree decompositions, and demonstrate via experimental evaluation on various datasets that our proposed algorithm is more accurate than sampling algorithms.

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Wang, Tianqi, Houping Xiao, Fenglong Ma, and Jing Gao. "IProWA: A Novel Probabilistic Graphical Model for Crowdsourcing Aggregation." In 2019 IEEE International Conference on Big Data (Big Data). IEEE, 2019. http://dx.doi.org/10.1109/bigdata47090.2019.9005518.

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Yang, Michael Ying. "A Generic Probabilistic Graphical Model for Region-based Scene Interpretation." In International Conference on Computer Vision Theory and Applications. SCITEPRESS - Science and and Technology Publications, 2015. http://dx.doi.org/10.5220/0005341004860491.

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Reports on the topic "Probabilistic Graphical Model"

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Wang, Haiqin, and Marek Druzdzel. Cloud Library for Directed Probabilistic Graphical Models. Fort Belvoir, VA: Defense Technical Information Center, October 2014. http://dx.doi.org/10.21236/ada611690.

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Mohan, Karthika, and Judea Pearl. Graphical Models for Recovering Probabilistic and Causal Queries from Missing Data. Fort Belvoir, VA: Defense Technical Information Center, November 2014. http://dx.doi.org/10.21236/ada614408.

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