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Relevant bibliographies by topics / First order Markov model

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Academic literature on the topic 'First order Markov model'

Author: Grafiati

Published: 4 June 2021

Last updated: 13 February 2022

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Journal articles on the topic "First order Markov model"

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Ye, Fei, and Yifei Wang. "A Novel Method for Decoding Any High-Order Hidden Markov Model." Discrete Dynamics in Nature and Society 2014 (2014): 1–6. http://dx.doi.org/10.1155/2014/231704.

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This paper proposes a novel method for decoding any high-order hidden Markov model. First, the high-order hidden Markov model is transformed into an equivalent first-order hidden Markov model by Hadar’s transformation. Next, the optimal state sequence of the equivalent first-order hidden Markov model is recognized by the existing Viterbi algorithm of the first-order hidden Markov model. Finally, the optimal state sequence of the high-order hidden Markov model is inferred from the optimal state sequence of the equivalent first-order hidden Markov model. This method provides a unified algorithm framework for decoding hidden Markov models including the first-order hidden Markov model and any high-order hidden Markov model.

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Shen, Wei. "On the First-Order Markov Model for the PLC Fading Channel." Advanced Materials Research 756-759 (September 2013): 2644–48. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.2644.

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The power line communications (PLC) channel is noisy one, which can be modeled by the Markov process. The order is the key concerning when used the Markov process. the level of complexity will be incurred from using higher order , while the first-order Markov models may lead to the less accurate channel response. In the paper, the first-order Markov channel is under thoroughly discussion, and it can provide a mathematically tractable model for time-varying channels and uses only the received SNR of the symbol immediately preceding the current one. With the first-order Markov chain, given the information of the symbol immediately preceding the current one, any other previous symbol should be independent of the current one. We show that given the information corresponding to the previous symbol, the amount of uncertainty remaining in the current symbol should be negligible. That means the first-order of Markov process is enough when modeled the PLC channel.

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Hawkins, Daniel B. "A First-Order Markov-Chain Model of Zeolite Crystallization1." Clays and Clay Minerals 37, no. 5 (1989): 433–38. http://dx.doi.org/10.1346/ccmn.1989.0370506.

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Dahan, Fadl, Ameur Touir, and Hassan Mathkour. "First Order Hidden Markov Model for Automatic Arabic Name Entity Recognition." International Journal of Computer Applications 123, no. 7 (2015): 37–40. http://dx.doi.org/10.5120/ijca2015905397.

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Eggar, M. H. "Validity of fitting a first-order Markov chain model to data." Journal of the Royal Statistical Society: Series D (The Statistician) 51, no. 2 (2002): 259–65. http://dx.doi.org/10.1111/1467-9884.00316.

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Korte, Alvin O. "A First Order Markov Model for Use in the Human Services." Computers in Human Services 6, no. 4 (1990): 299–312. http://dx.doi.org/10.1300/j407v06n04_07.

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Pati, Avadh, Awadhesh Kumar, and Dinesh Chandra. "Suboptimal Control Using Model Order Reduction." Chinese Journal of Engineering 2014 (February 3, 2014): 1–5. http://dx.doi.org/10.1155/2014/797581.

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A Padé approximation based technique for designing a suboptimal controller is presented. The technique uses matching of both time moments and Markov parameters for model order reduction. In this method, the suboptimal controller is first derived for reduced order model and then implemented for higher order plant by partial feedback of measurable states.

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Toivonen, Jarkko, Pratyush K. Das, Jussi Taipale, and Esko Ukkonen. "MODER2: first-order Markov modeling and discovery of monomeric and dimeric binding motifs." Bioinformatics 36, no. 9 (2020): 2690–96. http://dx.doi.org/10.1093/bioinformatics/btaa045.

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Abstract Motivation Position-specific probability matrices (PPMs, also called position-specific weight matrices) have been the dominating model for transcription factor (TF)-binding motifs in DNA. There is, however, increasing recent evidence of better performance of higher order models such as Markov models of order one, also called adjacent dinucleotide matrices (ADMs). ADMs can model dependencies between adjacent nucleotides, unlike PPMs. A modeling technique and software tool that would estimate such models simultaneously both for monomers and their dimers have been missing. Results We present an ADM-based mixture model for monomeric and dimeric TF-binding motifs and an expectation maximization algorithm MODER2 for learning such models from training data and seeds. The model is a mixture that includes monomers and dimers, built from the monomers, with a description of the dimeric structure (spacing, orientation). The technique is modular, meaning that the co-operative effect of dimerization is made explicit by evaluating the difference between expected and observed models. The model is validated using HT-SELEX and generated datasets, and by comparing to some earlier PPM and ADM techniques. The ADM models explain data slightly better than PPM models for 314 tested TFs (or their DNA-binding domains) from four families (bHLH, bZIP, ETS and Homeodomain), the ADM mixture models by MODER2 being the best on average. Availability and implementation Software implementation is available from https://github.com/jttoivon/moder2. Supplementary information Supplementary data are available at Bioinformatics online.

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Yu, Yi, and Haiquan Zhao. "A joint-optimization NSAF algorithm based on the first-order Markov model." Signal, Image and Video Processing 11, no. 3 (2016): 509–16. http://dx.doi.org/10.1007/s11760-016-0988-0.

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Demirekler, M. "On the first order equalization of hidden Markov models." IEEE Transactions on Signal Processing 43, no. 6 (1995): 1522–24. http://dx.doi.org/10.1109/78.388869.

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Dissertations / Theses on the topic "First order Markov model"

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Wong, Ho-ting, and 黃浩霆. "Hand-written Chinese character recognition by first and second order Hidden Markov Models and radical modeling." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2003. http://hub.hku.hk/bib/B27770862.

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Yontay, Petek. "A Two-sided Cusum For First-order Integer-valued Autoregressive Processes Of Poisson Counts." Master's thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12613385/index.pdf.

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Count data are often encountered in manufacturing and service industries due to ease of data collection. These counts can be useful in process monitoring to detect shifts of a process from an in-control state to various out-of-control states. It is usually assumed that the observations are independent and identically distributed. However, in practice, observations may be autocorrelated and this may adversely affect the performance of the control charts developed under the assumption of independence. In this thesis, the cumulative sum (CUSUM) control chart for monitoring autocorrelated processes of counts is investigated. To describe the autocorrelation structure of counts, a Poisson integer-valued autoregressive moving average model of order 1, Poisson INAR(1), is employed. Changes in the process mean in both positive and negative directions are taken into account while designing the CUSUM chart. A trivariate Markov Chain approach is utilized for evaluating the performance of the chart.

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Alkhatib, Rami. "Human locomotion analysis, classification and modeling of normal and pathological vertical ground reaction force signals in elderly." Thesis, Lyon, 2016. http://www.theses.fr/2016LYSES029/document.

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La marche est définie par des séquences de gestes cycliques et répétées. Il a été déjà montré que la vitesse et la variabilité de ces séquences peuvent révéler des aptitudes ou des défaillances motrices. L’originalité de ce travail est alors d’analyser et de caractériser les foulées de sujets âgés à partir des signaux de pression issus de semelles instrumentées lors de la marche, au moyen d’outils de traitement du signal. Une étude préliminaire, sur les signaux de pression générés lors de la marche, nous a permis de mettre en évidence le caractère cyclo-stationnaire de ces signaux. Ces paramètres sont testées sur une population de 47 sujets. Tout d'abord, nous avons commencé par un prétraitement des signaux et nous avons montré dans la première de cette thèse que le filtrage peut éliminer une partie vitale du signal. C’est pourquoi un filtre adaptatif basé sur la décomposition en mode empirique a été conçu. Les points de retournement ont été filtrés ensuite en utilisant une technique temps-fréquence appelée «synochronosqueezing». Nous avons également montré que le contenu des signaux de force de marche est fortement affecté par des paramètres inquantifiables tels que les tâches cognitives qui les rendent difficiles à normaliser. C’est pourquoi les paramètres extraits de nos signaux sont tous dérivées par une comparaison inter-sujet. Par exemple, nous avons assimilé la différence dans la répartition de poids entre les pieds. Il est également recommandé dans ce travail de choisir le centre des capteurs plutôt que de compter sur la somme des forces issues du réseau de capteurs pour la classification. Ensuite, on a montré que l’hypothèse de la marche équilibrée et déséquilibrée peut améliorer les résultats de la classification. Le potentiel de cette hypothèse est montré à l'aide de la répartition du poids ainsi que le produit de l'âge × vitesse dans le premier classificateur et la corrélation dans le second classificateur. Une simulation de la série temporelle de VGRF basé sur une version modifiée du modèle de Markov non stationnaire, du premier ordre est ensuite dérivée. Ce modèle prédit les allures chez les sujets normaux et suffisamment pour les allures des sujets de Parkinson. On a trouvé que les trois modes: temps, fréquence et espace sont très utiles pour l’analyse des signaux de force, c’est pourquoi l’analyse de facteurs parallèles est introduite comme étant une méthode de tenseur qui peut être utilisée dans le futur<br>Walking is defined as sequences of repetitive cyclic gestures. It was already shown that the speed and the variability of these sequences can reveal abilities or motorskill failures. The originality of this work is to analyze and characterize the steps of elderly persons by using pressure signals. In a preliminary study, we showed that pressure signals are characterized by cyclostationarity. In this study, we intend to exploit the nonstationarity of the signals in a search for new indicators that can help in gait signal classification between normal and Parkinson subjects in the elderly population. These parameters are tested on a population of 47 subjects. First, we started with preprocessing the vertical ground reaction force (VGRF) signals and showed in this first part of the thesis that filtering can remove a vital part of the signal. That is why an adaptive filter based on empirical mode decomposition (EMD) was built. Turning points are filtered using synochronosqueezing of time-frequency representations of the signal. We also showed that the content of gait force signals is highly affected by unquantifiable parameter such as cognitive tasks which make them hard to be normalized. That is why features being extracted are derived from inter-subject comparison. For example we equated the difference in the load distribution between feet. It is also recommended in this work to choose the mid-sensor rather than relying on summation of forces from array of sensors for classification purposes. A hypothesis of balanced and unbalanced gait is verified to be potential in improving the classification accuracy. The power of this hypothesis is shown by using the load distribution and Age×Speed in the first classifier and the correlation in the second classifier. A time series simulation of VGRF based on a modified version of nonstationary- Markov model of first order is derived. This model successfully predict gaits in normal subjects and fairly did in Parkinson’s gait. We found out that the three modes: time, frequency and space are helpful in analyzing force signals that is why parallel factor analysis is introduced as a tensor method to be used in a future work

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Schimert, James. "A high order hidden Markov model /." Thesis, Connect to this title online; UW restricted, 1992. http://hdl.handle.net/1773/8939.

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Schwardt, Ludwig. "Efficient Mixed-Order Hidden Markov Model Inference." Thesis, Link to the online version, 2007. http://hdl.handle.net/10019/709.

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Jindasawat, Jutaporn. "Testing the order of a Markov chain model." Thesis, University of Newcastle Upon Tyne, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.446197.

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Nelson, Tim. "First-Order Models for Configuration Analysis." Digital WPI, 2013. https://digitalcommons.wpi.edu/etd-dissertations/452.

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Our world teems with networked devices. Their configuration exerts an ever-expanding influence on our daily lives. Yet correctly configuring systems, networks, and access-control policies is notoriously difficult, even for trained professionals. Automated static analysis techniques provide a way to both verify a configuration's correctness and explore its implications. One such approach is scenario-finding: showing concrete scenarios that illustrate potential (mis-)behavior. Scenarios even have a benefit to users without technical expertise, as concrete examples can both trigger and improve users' intuition about their system. This thesis describes a concerted research effort toward improving scenario-finding tools for configuration analysis. We developed Margrave, a scenario-finding tool with special features designed for security policies and configurations. Margrave is not tied to any one specific policy language; rather, it provides an intermediate input language as expressive as first-order logic. This flexibility allows Margrave to reason about many different types of policy. We show Margrave in action on Cisco IOS, a common language for configuring firewalls, demonstrating that scenario-finding with Margrave is useful for debugging and validating real-world configurations. This thesis also presents a theorem showing that, for a restricted subclass of first-order logic, if a sentence is satisfiable then there must exist a satisfying scenario no larger than a computable bound. For such sentences scenario-finding is complete: one can be certain that no scenarios are missed by the analysis, provided that one checks up to the computed bound. We demonstrate that many common configurations fall into this subclass and give algorithmic tests for both sentence membership and counting. We have implemented both in Margrave. Aluminum is a tool that eliminates superfluous information in scenarios and allows users' goals to guide which scenarios are displayed. We quantitatively show that our methods of scenario-reduction and exploration are effective and quite efficient in practice. Our work on Aluminum is making its way into other scenario-finding tools. Finally, we describe FlowLog, a language for network programming that we created with analysis in mind. We show that FlowLog can express many common network programs, yet demonstrate that automated analysis and bug-finding for FlowLog are both feasible as well as complete.

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Engelbrecht, Herman A. "Efficient Decoding of High-order Hidden Markov Models." Thesis, Stellenbosch : University of Stellenbosch, 2007. http://hdl.handle.net/10019.1/1095.

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Thesis (PhD (Electrical and Electronic Engineering))--University of Stellenbosch, 2007.<br>Most speech recognition and language identification engines are based on hidden Markov models (HMMs). Higher-order HMMs are known to be more powerful than first-order HMMs, but have not been widely used because of their complexity and computational demands. The main objective of this dissertation was to develop a more time-efficient method of decoding high-order HMMs than the standard Viterbi decoding algorithm currently in use. We proposed, implemented and evaluated two decoders based on the Forward-Backward Search (FBS) paradigm, which incorporate information obtained from low-order HMMs. The first decoder is based on time-synchronous Viterbi-beam decoding where we wish to base our state pruning on the complete observation sequence. The second decoder is based on time-asynchronous A* search. The choice of heuristic is critical to the A* search algorithms and a novel, task-independent heuristic function is presented. The experimental results show that both these proposed decoders result in more time-efficient decoding of the fully-connected, high-order HMMs that were investigated. Three significant facts have been uncovered. The first is that conventional forward Viterbi-beam decoding of high-order HMMs is not as computationally expensive as is commonly thought. The second (and somewhat surprising) fact is that backward decoding of conventional, high-order left-context HMMs is significantly more expensive than the conventional forward decoding. By developing the right-context HMM, we showed that the backward decoding of a mathematically equivalent right-context HMM is as expensive as the forward decoding of the left-context HMM. The third fact is that the use of information obtained from low-order HMMs significantly reduces the computational expense of decoding high-order HMMs. The comparison of the two new decoders indicate that the FBS-Viterbi-beam decoder is more time-efficient than the A* decoder. The FBS-Viterbi-beam decoder is not only simpler to implement, it also requires less memory than the A* decoder. We suspect that the broader research community regards the Viterbi-beam algorithm as the most efficient method of decoding HMMs. We hope that the research presented in this dissertation will result in renewed investigation into decoding algorithms that are applicable to high-order HMMs.

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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.

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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

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Xu, Ying. "Model checking for a first-order temporal logic using multiway decision graphs." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape7/PQDD_0010/NQ52197.pdf.

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Books on the topic "First order Markov model"

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Jacobs, Patricia A. Estimation of the probability of a long time to the first entrance to a state in a semi-Markov model. Naval Postgraduate School, 1987.

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Reinhardt, Elizabeth D. First Order Fire Effects Model: FOFEM 4.0, user's guide. U.S. Dept. of Agriculture, Forest Sevice, Intermountain Research Station, 1997.

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Nguyen, Van O. Analysis of the U.S. Marine Corps' steady state Markov model for forecasting annual first-term enlisted classification requirements. Naval Postgraduate School, 1997.

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Hoque, Asraul. The exact multiperiod mean-square forecast error for the first-order autoregressive model. London School of Economics, 1986.

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Weaver, George. Henkin-Keisler models. Kluwer Academic Publishers, 1997.

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Weaver, George. Henkin-Keisler Models {Mathematics and Its Applications (Kluwer Academic Publishers) ; V. 392}. Springer, 1997.

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Simple theories and hyperimaginaries. Cambridge University Press, 2011.

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Magnus, Jan R. The exact multiperiod mean-square forecast error for the first-order autoregressive model with an intercept. International Centre for Economics and Related Disciplines, 1988.

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Abou-Taleb, A. A. A microwave model for investigating first and second order electromagnetic scattering from gravity water waves on the surface. University of Birmingham, 1985.

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Poletaeva, Vladislava. Economics of sustainable industrial growth: concept, problems and possible mechanisms of formation. INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1086387.

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The monograph examines the issues of transformation of the Russian economy from raw materials export model to a model of sustainable industrial growth. In the first Chapter of the work the author formulates the definition of sustainable economy growth and the expediency of its formation, analyzes the problems that hinder the transformation of national economic system into a model for sustainable industrial growth, and identified possible mechanisms of such transformation. In the second Chapter, in order to determine the sources of the implementation of the financial mechanism of forming of economy of sustainable industrial growth, the author assesses financial potential of economic entities and analyzes the role of the banking sector and the state to invest resources in the Russian economy. In the third Chapter the author provides the rationale (for the decision of task of forming of economy of industrial growth) for the development of cooperation in the banking sector and the state in the financing of manufacturing industry on the basis of realization of interests of all key stakeholders of such projects, identifies the interests of the state, banking sector and manufacturing industries and estimated the fullness of their realization in the framework of the existing mechanisms of the banking and government lending to the economy. Designed for teachers, students of economic specialties, as well as anyone interested in the problems of development of economy in modern conditions.

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Book chapters on the topic "First order Markov model"

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Conejo, S., A. Morata, and F. Valero. "First Order Markov Chain Model and Rainfall Sequences in several Stations of Spain." In Detecting and Modelling Regional Climate Change. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-662-04313-4_36.

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Holtzen, Steven, Sebastian Junges, Marcell Vazquez-Chanlatte, Todd Millstein, Sanjit A. Seshia, and Guy Van den Broeck. "Model Checking Finite-Horizon Markov Chains with Probabilistic Inference." In Computer Aided Verification. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81688-9_27.

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AbstractWe revisit the symbolic verification of Markov chains with respect to finite horizon reachability properties. The prevalent approach iteratively computes step-bounded state reachability probabilities. By contrast, recent advances in probabilistic inference suggest symbolically representing all horizon-length paths through the Markov chain. We ask whether this perspective advances the state-of-the-art in probabilistic model checking. First, we formally describe both approaches in order to highlight their key differences. Then, using these insights we develop Rubicon, a tool that transpiles Prism models to the probabilistic inference tool . Finally, we demonstrate better scalability compared to probabilistic model checkers on selected benchmarks. All together, our results suggest that probabilistic inference is a valuable addition to the probabilistic model checking portfolio, with Rubicon as a first step towards integrating both perspectives.

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Kurtulus, Kemal. "Utilization of the First Order Markov Model in Predicting the Market Share of a Newspaper." In Proceedings of the 1983 Academy of Marketing Science (AMS) Annual Conference. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16937-8_124.

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Prestel, Alexander, and Charles N. Delzell. "First-Order Logic." In Mathematical Logic and Model Theory. Springer London, 2011. http://dx.doi.org/10.1007/978-1-4471-2176-3_2.

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Gergely, Tamás, and László Úry. "Logic and Model Theory." In First-Order Programming Theories. Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-642-58205-9_2.

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Bohn, Jürgen, Werner Damm, Orna Grumberg, Hardi Hungar, and Karen Laster. "First-Order-CTL Model Checking." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-540-49382-2_27.

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Libkin, Leonid. "Complexity of First-Order Logic." In Elements of Finite Model Theory. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-662-07003-1_6.

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Parag, Toufiq, and Ahmed Elgammal. "Higher Order Markov Networks for Model Estimation." In Advances in Visual Computing. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24028-7_23.

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Harrison, John. "Formalizing basic first order model theory." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0055135.

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Keane, Robert E., and Duncan Lutes. "First-Order Fire Effects Model (FOFEM)." In Encyclopedia of Wildfires and Wildland-Urban Interface (WUI) Fires. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-51727-8_74-1.

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Conference papers on the topic "First order Markov model"

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Kailas, Aravind, Chia-Chin Chong, and Fujio Watanabe. "A First-Order Markov Model for Wellness Mobile Applications." In 2010 IEEE 71st Vehicular Technology Conference. IEEE, 2010. http://dx.doi.org/10.1109/vetecs.2010.5493658.

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SHEN, Wei. "On the First-Order Markov Model for the PLC Fading Channel." In 2nd International Conference on Computer and Information Applications (ICCIA 2012). Atlantis Press, 2012. http://dx.doi.org/10.2991/iccia.2012.24.

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Linstead, Erik, Lindsey Hughes, Cristina Lopes, and Pierre Baldi. "Capturing Java naming conventions with first-order Markov models." In 2009 IEEE 17th International Conference on Program Comprehension (ICPC). IEEE, 2009. http://dx.doi.org/10.1109/icpc.2009.5090074.

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Taghva, Kazem, Srijana Poudel, and Spandana Malreddy. "Post processing with first- and second-order hidden Markov models." In IS&T/SPIE Electronic Imaging, edited by Richard Zanibbi and Bertrand Coüasnon. SPIE, 2013. http://dx.doi.org/10.1117/12.2006500.

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van Bremen, Timothy, and Ondrej Kuzelka. "Approximate Weighted First-Order Model Counting: Exploiting Fast Approximate Model Counters and Symmetry." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/587.

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We study the symmetric weighted first-order model counting task and present ApproxWFOMC, a novel anytime method for efficiently bounding the weighted first-order model count of a sentence given an unweighted first-order model counting oracle. The algorithm has applications to inference in a variety of first-order probabilistic representations, such as Markov logic networks and probabilistic logic programs. Crucially for many applications, no assumptions are made on the form of the input sentence. Instead, the algorithm makes use of the symmetry inherent in the problem by imposing cardinality constraints on the number of possible true groundings of a sentence's literals. Realising the first-order model counting oracle in practice using the approximate hashing-based model counter ApproxMC3, we show how our algorithm is competitive with existing approximate and exact techniques for inference in first-order probabilistic models. We additionally provide PAC guarantees on the accuracy of the bounds generated.

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Di Giorgio, V., R. Langella, A. Testa, S. Z. Djokic, and M. Zou. "First Order Non-homogeneous Markov Chain Model for Generation of Wind Speed and Direction Synthetic Time Series." In 2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS). IEEE, 2020. http://dx.doi.org/10.1109/pmaps47429.2020.9183446.

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Nasehi, Saadat, and Hossein Pourghassem. "EEG Signal Compression Based on Adaptive Arithmetic Coding and First-Order Markov Model for an Ambulatory Monitoring System." In 2012 4th International Conference on Computational Intelligence and Communication Networks (CICN). IEEE, 2012. http://dx.doi.org/10.1109/cicn.2012.103.

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Annavajjala, Ramesh, A. Chockalingam, Pamela Cosman, and Laurence Milstein. "First-order Markov Models for Packet Transmission on Rayleigh Fading Channels with DPSK/NCFSK Modulation." In 2006 IEEE International Symposium on Information Theory. IEEE, 2006. http://dx.doi.org/10.1109/isit.2006.261663.

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Gjika (Dhamo), Eralda, Lule Basha, Xhensilda Allka, and Aurora Ferrja. "Predicting the Albanian economic development using multivariate Markov chain model." In 11th International Scientific Conference „Business and Management 2020“. VGTU Technika, 2020. http://dx.doi.org/10.3846/bm.2020.581.

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In this work, the economic development and relation to social and demography indices in Albania were studied. Four time series (yearly data for the period 1995–2018) were considered: consumer price index (CPI), unemployment rate, inflation and life expectancy. In our approach, a first and fifth order multivariate Markov chain model was proposed to predict the economic situation in Albania in the proceedings years. Tests and accuracy analysis of the model were performed. The prediction probabilities fall in the interval of 0.47 to 0.52 and the accuracy of both models is 75%. Our approach is a short term probability forecast model that can be used by the policymakers to evaluate and undertake initiatives to improve the situation in the country.

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Merrell, David, Aws Albarghouthi, and Loris D'Antoni. "Weighted Model Integration with Orthogonal Transformations." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/643.

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Weighted model counting and integration (WMC/WMI) are natural problems to which we can reduce many probabilistic inference tasks, e.g., in Bayesian networks, Markov networks, and probabilistic programs. Typically, we are given a first-order formula, where each satisfying assignment is associated with a weight---e.g., a probability of occurrence---and our goal is to compute the total weight of the formula. In this paper, we target exact inference techniques for WMI that leverage the power of satisfiability modulo theories (SMT) solvers to decompose a first-order formula in linear real arithmetic into a set of hyperrectangular regions whose weight is easy to compute. We demonstrate the challenges of hyperrectangular decomposition and present a novel technique that utilizes orthogonal transformations to transform formulas in order to enable efficient inference. Our evaluation demonstrates our technique's ability to improve the time required to achieve exact probability bounds.

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Reports on the topic "First order Markov model"

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Culotta, Aron, and Andrew McCallum. Practical Markov Logic Containing First-Order Quantifiers With Application to Identity Uncertainty. Defense Technical Information Center, 2005. http://dx.doi.org/10.21236/ada440385.

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Powell, Gerald M., and Charles F. Schmidt. A First-Order Computational Model of Human Operational Planning. Defense Technical Information Center, 1988. http://dx.doi.org/10.21236/ada396315.

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Reinhardt, Elizabeth D., Robert E. Keane, and James K. Brown. First Order Fire Effects Model: FOFEM 4.0, user's guide. U.S. Department of Agriculture, Forest Service, Intermountain Research Station, 1997. http://dx.doi.org/10.2737/int-gtr-344.

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Dewald, Lee S., Peter A. Lewis, and Ed McKenzie. A Bivariate First Order Autoregressive Time Series Model in Exponential Variables (BEAR(1)). Defense Technical Information Center, 1986. http://dx.doi.org/10.21236/ada177055.

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Hastings, D. E. Stochastic model of a first-order nonequilibrium phase transition in a magnetic fusion device. Office of Scientific and Technical Information (OSTI), 1985. http://dx.doi.org/10.2172/5305891.

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Anderson, T. W., and R. P. Mentz. Iterative Procedures for Exact Maximum Likelihood Estimation in the First-Order Gaussian Moving Average Model. Defense Technical Information Center, 1990. http://dx.doi.org/10.21236/ada230812.

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Huckaby, Dale A., and L. Blum. A Model for Sequential First Order Phage Transitions Occurring in the Underpotential Deposition of Metals,. Defense Technical Information Center, 1991. http://dx.doi.org/10.21236/ada235623.

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Sutherland, Steve. Fuels planning: science synthesis and integration; environmental consequences fact sheet 02: First Order Fire Effects Model (FOFEM). U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station, 2004. http://dx.doi.org/10.2737/rmrs-rn-23-v2.

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McNab, W. W. Jr. First-order kinetics-controlled multiple species reactive transport of dissolved organic compounds in groundwater: Development and application of a numerical model. Office of Scientific and Technical Information (OSTI), 1990. http://dx.doi.org/10.2172/6628114.

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Douglas, Thomas, Merritt Turetsky, and Charles Koven. Increased rainfall stimulates permafrost thaw across a variety of Interior Alaskan boreal ecosystems. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/41050.

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Earth’s high latitudes are projected to experience warmer and wetter summers in the future but ramifications for soil thermal processes and permafrost thaw are poorly understood. Here we present 2750 end of summer thaw depths representing a range of vegetation characteristics in Interior Alaska measured over a 5-year period. This included the top and third wettest summers in the 91-year record and three summers with precipitation close to mean historical values. Increased rainfall led to deeper thaw across all sites with an increase of 0.7 ± 0.1 cm of thaw per cm of additional rain. Disturbed and wetland sites were the most vulnerable to rain-induced thaw with ~1 cm of surface thaw per additional 1 cm of rain. Permafrost in tussock tundra, mixed forest, and conifer forest was less sensitive to rain-induced thaw. A simple energy budget model yields seasonal thaw values smaller than the linear regression of our measurements but provides a first-order estimate of the role of rain-driven sensible heat fluxes in high-latitude terrestrial permafrost. This study demonstrates substantial permafrost thaw from the projected increasing summer precipitation across most of the Arctic region.

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