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Academic literature on the topic 'Mixture Markov Model'

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Published: 9 March 2023

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Journal articles on the topic "Mixture Markov Model"

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Ming-Fang Yen, Amy, and Tony Hsiu-Hsi Chen. "Mixture Multi-state Markov Regression Model." Journal of Applied Statistics 34, no. 1 (January 2007): 11–21. http://dx.doi.org/10.1080/02664760600994711.

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Jang, Yoonsun, and Allan S. Cohen. "The Impact of Markov Chain Convergence on Estimation of Mixture IRT Model Parameters." Educational and Psychological Measurement 80, no. 5 (January 9, 2020): 975–94. http://dx.doi.org/10.1177/0013164419898228.

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A nonconverged Markov chain can potentially lead to invalid inferences about model parameters. The purpose of this study was to assess the effect of a nonconverged Markov chain on the estimation of parameters for mixture item response theory models using a Markov chain Monte Carlo algorithm. A simulation study was conducted to investigate the accuracy of model parameters estimated with different degree of convergence. Results indicated the accuracy of the estimated model parameters for the mixture item response theory models decreased as the number of iterations of the Markov chain decreased. In particular, increasing the number of burn-in iterations resulted in more accurate estimation of mixture IRT model parameters. In addition, the different methods for monitoring convergence of a Markov chain resulted in different degrees of convergence despite almost identical accuracy of estimation.
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Danaher, Peter J. "A Markov Mixture Model for Magazine Exposure." Journal of the American Statistical Association 84, no. 408 (December 1989): 922–26. http://dx.doi.org/10.1080/01621459.1989.10478856.

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Labeeuw, Wouter, and Geert Deconinck. "Residential Electrical Load Model Based on Mixture Model Clustering and Markov Models." IEEE Transactions on Industrial Informatics 9, no. 3 (August 2013): 1561–69. http://dx.doi.org/10.1109/tii.2013.2240309.

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Berthelsen, Kasper K., Laird A. Breyer, and Gareth O. Roberts. "Perfect posterior simulation for mixture and hidden Markov models." LMS Journal of Computation and Mathematics 13 (August 10, 2010): 246–59. http://dx.doi.org/10.1112/s1461157007000022.

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AbstractIn this paper we present an application of the read-once coupling from the past algorithm to problems in Bayesian inference for latent statistical models. We describe a method for perfect simulation from the posterior distribution of the unknown mixture weights in a mixture model. Our method is extended to a more general mixture problem, where unknown parameters exist for the mixture components, and to a hidden Markov model.
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Zaki, Ahmad, Wahidah Sanusi, and Saiful Bahri. "Model Rantai Markov dan Model ARIMA serta Kombinasinya dalam Memprediksi Curah Hujan di Kota Makassar." Journal of Mathematics, Computations, and Statistics 1, no. 1 (May 17, 2019): 8. http://dx.doi.org/10.35580/jmathcos.v1i1.9169.

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Abstrak. Curah hujan merupakan suatu data deret waktu yang bersifat kontinu, namun juga dapat diformulasikan sebagai peubah diskrit yaitu dengan menggolongkan suatu hari menjadi hujan dan tidak hujan. Curah hujan yang dicatat oleh pos hujan dapat digunakan untuk memprediksi curah hujan pada waktu yang akan datang melalui pemodelan deret waktu ARIMA musiman, Rantai Markov atau dengan campuran keduanya. Proses Markov merupakan suatu sistem stokastik di mana kejadian di masa yang akan datang bergantung pada kejadian sesaat sebelumnya Deret waktu merupakan serangkaian data yang disusun menurut urutan waktu Tujuan penelitian ini adalah untuk memodelkan dan memprediksi curah hujan dengan campuran Rantai Markov dan model deret waktu. Data yang digunakan dalam penelitian ini adalah curah hujan bulanan kota Makassar tahun 2007 sampai 2017. Campuran model deret waktu lebih sesuai digunakan untuk memprediksi curah hujan bulanan dibandingkan dengan pemodelan deret waktu saja hal ini dapat dilihat dai nilai MSE.Kata Kunci: Rantai Markov, Deret Waktu, ARIMA MusimanAbstract. Rainfall is a time series data that is continuous, but can also be formulated as a discrete variable that is by classifying one day as rainy and not rainy. Rainfall recorded by rain posts can be used to predict rainfall in the future through seasonal ARIMA time series modeling, Markov Chain or with a mixture of both. The Markov process is a stochastic system in which future events depend on the events of the previous moment. The time series is a series of data arranged in time sequence. The purpose of this study is to model and predict rainfall with a mixture of Markov Chains and time series models. The data used in this study is the monthly rainfall of Makassar city in 2007 to 2017. A mixture of time series models is more suitable to be used to predict monthly rainfall compared to modeling time series. This can be seen from the MSE value.Keywords: Markov chain, Time Series, seasonal ARIMA.
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Yuan, Shenfang, Jinjin Zhang, Jian Chen, Lei Qiu, and Weibo Yang. "A uniform initialization Gaussian mixture model–based guided wave–hidden Markov model with stable damage evaluation performance." Structural Health Monitoring 18, no. 3 (June 29, 2018): 853–68. http://dx.doi.org/10.1177/1475921718783652.

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During practical applications, the time-varying service conditions usually lead to difficulties in properly interpreting structural health monitoring signals. The guided wave–hidden Markov model–based damage evaluation method is a promising approach to address the uncertainties caused by the time-varying service condition. However, researches that have been performed to date are not comprehensive. Most of these research studies did not introduce serious time-varying factors, such as those that exist in reality, and hidden Markov model was applied directly without deep consideration of the performance improvement of hidden Markov model itself. In this article, the training stability problem when constructing the guided wave–hidden Markov model initialized by usually adopted k-means clustering method and its influence to damage evaluation were researched first by applying it to fatigue crack propagation evaluation of an attachment lug. After illustrating the problem of stability induced by k-means clustering, a novel uniform initialization Gaussian mixture model–based guided wave–hidden Markov model was proposed that provides steady and reliable construction of the guided wave–hidden Markov model. The advantage of the proposed method is demonstrated by lug fatigue crack propagation evaluation experiments.
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Tran, Dat, Wanli Ma, and Dharmendra Sharma. "Fuzzy Observable Markov Models for Pattern Recognition." Journal of Advanced Computational Intelligence and Intelligent Informatics 11, no. 6 (July 20, 2007): 662–67. http://dx.doi.org/10.20965/jaciii.2007.p0662.

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This paper presents a mathematical framework for fuzzy discrete and continuous observable Markov models (OMMs) and their applications to written language, spam email and typist recognition. Experimental results show that the proposed OMMs are more effective than models such as vector quantization, Gaussian mixture model and hidden Markov model.
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Chen, Zhongsheng, Yongmin Yang, Zheng Hu, and Qinghu Zeng. "Fault prognosis of complex mechanical systems based on multi-sensor mixtured hidden semi-Markov models." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 227, no. 8 (November 21, 2012): 1853–63. http://dx.doi.org/10.1177/0954406212467260.

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Accurate fault prognosis is of vital importance for condition-based maintenance. As to complex mechanical systems, multiple sensors are often used to collect condition signals and the observation process may rather be non-Gaussian and non-stationary. Traditional hidden semi-Markov models cannot provide adequate representation for multivariate non-Gaussian and non-stationary time series. The innovation of this article is to extend classical hidden semi-Markov models by modeling the observation as a linear mixture of non-Gaussian multi-sensor signals. The proposed model is called as a multi-sensor mixtured hidden semi-Markov model. Under this new framework, modified parameter re-estimation algorithms are derived in detail based on the complete-data expectation maximization algorithm. In the end the proposed prognostic methodology is validated on a practical bearing application. The experimental results show that the proposed method is indeed promising to obtain better prognostic performance than classical hidden semi-Markov models.
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Berchtold, André. "Confidence Intervals for the Mixture Transition Distribution (MTD) Model and Other Markovian Models." Symmetry 12, no. 3 (March 1, 2020): 351. http://dx.doi.org/10.3390/sym12030351.

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The Mixture Transition Distribution (MTD) model used for the approximation of high-order Markov chains does not allow a simple calculation of confidence intervals, and computationnally intensive methods based on bootstrap are generally used. We show here how standard methods can be extended to the MTD model as well as other models such as the Hidden Markov Model. Starting from existing methods used for multinomial distributions, we describe how the quantities required for their application can be obtained directly from the data or from one run of the E-step of an EM algorithm. Simulation results indicate that when the MTD model is estimated reliably, the resulting confidence intervals are comparable to those obtained from more demanding methods.
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Dissertations / Theses on the topic "Mixture Markov Model"

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Frühwirth-Schnatter, Sylvia. "Model Likelihoods and Bayes Factors for Switching and Mixture Models." SFB Adaptive Information Systems and Modelling in Economics and Management Science, WU Vienna University of Economics and Business, 2002. http://epub.wu.ac.at/474/1/document.pdf.

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In the present paper we discuss the problem of estimating model likelihoods from the MCMC output for a general mixture and switching model. Estimation is based on the method of bridge sampling (Meng and Wong, 1996), where the MCMC sample is combined with an iid sample from an importance density. The importance density is constructed in an unsupervised manner from the MCMC output using a mixture of complete data posteriors. Whereas the importance sampling estimator as well as the reciprocal importance sampling estimator are sensitive to the tail behaviour of the importance density, we demonstrate that the bridge sampling estimator is far more robust in this concern. Our case studies range from computing marginal likelihoods for a mixture of multivariate normal distributions, testing for the inhomogeneity of a discrete time Poisson process, to testing for the presence of Markov switching and order selection in the MSAR model. (author's abstract)
Series: Report Series SFB "Adaptive Information Systems and Modelling in Economics and Management Science"
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Wang, Xin, and n/a. "Research of mixture of experts model for time series prediction." University of Otago. Department of Information Science, 2005. http://adt.otago.ac.nz./public/adt-NZDU20070312.144924.

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For the prediction of chaotic time series, a dichotomy has arisen between local approaches and global approaches. Local approaches hold the reputation of simplicity and feasibility, but they generally do not produce a compact description of the underlying system and are computationally intensive. Global approaches have the advantage of requiring less computation and are able to yield a global representation of the studied time series. However, due to the complexity of the time series process, it is often not easy to construct a global model to perform the prediction precisely. In addition to these approaches, a combination of the global and local techniques, called mixture of experts (ME), is also possible, where a smaller number of models work cooperatively to implement the prediction. This thesis reports on research about ME models for chaotic time series prediction. Based on a review of the techniques in time series prediction, a HMM-based ME model called "Time-line" Hidden Markov Experts (THME) is developed, where the trajectory of the time series is divided into some regimes in the state space and regression models called local experts are applied to learn the mapping on the regimes separately. The dynamics for the expert combination is a HMM, however, the transition probabilities are designed to be time-varying and conditional on the "real time" information of the time series. For the learning of the "time-line" HMM, a modified Baum-Welch algorithm is developed and the convergence of the algorithm is proved. Different versions of the model, based on MLP, RBF and SVM experts, are constructed and applied to a number of chaotic time series on both one-step-ahead and multi-step-ahead predictions. Experiments show that in general THME achieves better generalization performance than the corresponding single models in one-step-ahead prediction and comparable to some published benchmarks in multi-step-ahead prediction. Various properties of THME, such as the feature selection for trajectory dividing, the clustering techniques for regime extraction, the "time-line" HMM for expert combination and the performance of the model when it has different number of experts, are investigated. A number of interesting future directions for this work are suggested, which include the feature selection for regime extraction, the model selection for transition probability modelling, the extension to distribution prediction and the application on other time series.
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Heinz, Daniel. "Hyper Markov Non-Parametric Processes for Mixture Modeling and Model Selection." Research Showcase @ CMU, 2010. http://repository.cmu.edu/dissertations/11.

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Markov distributions describe multivariate data with conditional independence structures. Dawid and Lauritzen (1993) extended this idea to hyper Markov laws for prior distributions. A hyper Markov law is a distribution over Markov distributions whose marginals satisfy the same conditional independence constraints. These laws have been used for Gaussian mixtures (Escobar, 1994; Escobar and West, 1995) and contingency tables (Liu and Massam, 2006; Dobra and Massam, 2009). In this paper, we develop a family of non-parametric hyper Markov laws that we call hyper Dirichlet processes, combining the ideas of hyper Markov laws and non-parametric processes. Hyper Dirichlet processes are joint laws with Dirichlet process laws for particular marginals. We also describe a more general class of Dirichlet processes that are not hyper Markov, but still contain useful properties for describing graphical data. The graphical Dirichlet processes are simple Dirichlet processes with a hyper Markov base measure. This class allows an extremely straight-forward application of existing Dirichlet knowledge and technology to graphical settings. Given the wide-spread use of Dirichlet processes, there are many applications of this framework waiting to be explored. One broad class of applications, known as Dirichlet process mixtures, has been used for constructing mixture densities such that the underlying number of components may be determined by the data (Lo, 1984; Escobar, 1994; Escobar and West, 1995). I consider the use of the new graphical Dirichlet process in this setting, which imparts a conditional independence structure inside each component. In other words, given the component or cluster membership, the data exhibit the desired independence structure. We discuss two applications. Expanding on the work of Escobar and West (1995), we estimate a non-parametric mixture of Markov Gaussians using a Gibbs sampler. Secondly, we employ the Mode-Oriented Stochastic Search of Dobra and Massam (2009) for determining a suitable conditional independence model, focusing on contingency tables. In general, the mixing induced by a Dirichlet process does not drastically increase the complexity beyond that of a simpler Bayesian hierarchical models sans mixture components. We provide a specific representation for decomposable graphs with useful algorithms for local updates.
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Loza, Reyes Elisa. "Classification of phylogenetic data via Bayesian mixture modelling." Thesis, University of Bath, 2010. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.519916.

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Conventional probabilistic models for phylogenetic inference assume that an evolutionary tree,andasinglesetofbranchlengthsandstochasticprocessofDNA evolutionare sufficient to characterise the generating process across an entire DNA alignment. Unfortunately such a simplistic, homogeneous formulation may be a poor description of reality when the data arise from heterogeneous processes. A well-known example is when sites evolve at heterogeneous rates. This thesis is a contribution to the modelling and understanding of heterogeneityin phylogenetic data. Weproposea methodfor the classificationof DNA sites based on Bayesian mixture modelling. Our method not only accounts for heterogeneous data but also identifies the underlying classes and enables their interpretation. We also introduce novel MCMC methodology with the same, or greater, estimation performance than existing algorithms but with lower computational cost. We find that our mixture model can successfully detect evolutionary heterogeneity and demonstrate its direct relevance by applying it to real DNA data. One of these applications is the analysis of sixteen strains of one of the bacterial species that cause Lyme disease. Results from that analysis have helped understanding the evolutionary paths of these bacterial strains and, therefore, the dynamics of the spread of Lyme disease. Our method is discussed in the context of DNA but it may be extendedto othertypesof molecular data. Moreover,the classification scheme thatwe propose is evidence of the breadth of application of mixture modelling and a step forwards in the search for more realistic models of theprocesses that underlie phylogenetic data.
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Koh, Maria. "Socioeconomic patterning of self-rated health trajectories in Canada: A mixture latent Markov model." Thesis, McGill University, 2012. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=110661.

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This thesis investigates the association between socioeconomic position and self-rated health trajectories among Canadians. Data come from the Survey of Labour and Income Dynamics (SLID), Panel 4 (year 2002 to 2008), conducted by Statistics Canada. These longitudinal data are analyzed using mixed latent Markov model which allows for modeling multiple trajectories of health. Goodness of fit tests showed three trajectories (good health, poor health, and fluctuating health) to provide the best fit to the data. The results show that more than three quarters of Canadians were in the constant good health trajectory whereas 13.95% and 7.99% of Canadians were respectively in the persistent ill health trajectory and fluctuating health trajectory. The relative risk ratios indicate that increasing income and education are independently associated with a greater likelihood of belonging to the persistent good health trajectory rather than the persistent poor health trajectory. Both associations accounted for possible confounders including gender, age, marital status, immigrant status and visible minority status. These results suggest that a socioeconomic gradient exists in the likelihood of belonging to given health trajectories. In addition, the use of mixed latent Markov model is robust in accounting for certain issues inherent to longitudinal analysis. Notably, the Markov chain models the dependency between repeated measurements within the same individual; it allows for the modeling of the latent variables estimate measurement error; the heterogeneity of the population is accounted by finite mixture modeling; and lastly, missing data are dealt with using full information maximum likelihood.
Cette thèse étudie l'association entre la position socioéconomique et les trajectoires de santé perçue parmi la population canadienne. Les données proviennent de l'Enquête sur la dynamique du travail et du revenu (EDTR) de Statistique Canada. Ces données longitudinales couvrant la période 2002-2008 sont analysées à l'aide de chaines de Markov avec variables latentes, qui permettent de modéliser les trajectoires de santé perçue des individus. Les résultats indiquent que plus de trois Canadiens sur quatre appartiennent à la trajectoire de bonne santé stable, alors que 13.95% et 7.99% des Canadiens se trouvent respectivement dans les trajectoires de mauvaise santé persistante et de santé instable. Les ratios de risque indiquent qu'il existe un gradient inverse entre le niveau de revenu et le degré d'instruction et le risque d'appartenir à la trajectoire de mauvaise santé plutôt qu'à celle de bonne santé. Cette association persiste suite à l'ajout des caractéristiques sociodémographiques telles le sexe, l'âge, et les statuts matrimonial, d'immigrant et de minorité visible. Ces résultats établissent la présence d'un gradient socioéconomique dans les trajectoires de santé, démonstration qui n'avait jusqu'à maintenant pas été faite au Canada. Qui plus est, les méthodes utilisées s'avèrent robustes pour l'analyse des données longitudinales et des problèmes qui y sont souvent associés. En effet, les chaines de Markov tiennent explicitement compte de la corrélation entre les réponses fournies à travers le temps par un même individu; l'hétérogénéité dans les trajectoires est prise en compte par un modèle pour un mélange fini de distributions (finite mixture model); les erreurs de mesure sont incorporées dans l'estimation des variables latentes; et enfin, les données manquantes sont estimées à l'aide de l'algorithme du maximum de vraisemblance à information complète (full information maximum likelihood).
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Kullmann, Emelie. "Speech to Text for Swedish using KALDI." Thesis, KTH, Optimeringslära och systemteori, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-189890.

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The field of speech recognition has during the last decade left the re- search stage and found its way in to the public market. Most computers and mobile phones sold today support dictation and transcription in a number of chosen languages.  Swedish is often not one of them. In this thesis, which is executed on behalf of the Swedish Radio, an Automatic Speech Recognition model for Swedish is trained and the performance evaluated. The model is built using the open source toolkit Kaldi.  Two approaches of training the acoustic part of the model is investigated. Firstly, using Hidden Markov Model and Gaussian Mixture Models and secondly, using Hidden Markov Models and Deep Neural Networks. The later approach using deep neural networks is found to achieve a better performance in terms of Word Error Rate.
De senaste åren har olika tillämpningar inom människa-dator interaktion och främst taligenkänning hittat sig ut på den allmänna marknaden. Många system och tekniska produkter stöder idag tjänsterna att transkribera tal och diktera text. Detta gäller dock främst de större språken och sällan finns samma stöd för mindre språk som exempelvis svenskan. I detta examensprojekt har en modell för taligenkänning på svenska ut- vecklas. Det är genomfört på uppdrag av Sveriges Radio som skulle ha stor nytta av en fungerande taligenkänningsmodell på svenska. Modellen är utvecklad i ramverket Kaldi. Två tillvägagångssätt för den akustiska träningen av modellen är implementerade och prestandan för dessa två är evaluerade och jämförda. Först tränas en modell med användningen av Hidden Markov Models och Gaussian Mixture Models och slutligen en modell där Hidden Markov Models och Deep Neural Networks an- vänds, det visar sig att den senare uppnår ett bättre resultat i form av måttet Word Error Rate.
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Tüchler, Regina. "Bayesian Variable Selection for Logistic Models Using Auxiliary Mixture Sampling." Department of Statistics and Mathematics, WU Vienna University of Economics and Business, 2006. http://epub.wu.ac.at/984/1/document.pdf.

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The paper presents an Markov Chain Monte Carlo algorithm for both variable and covariance selection in the context of logistic mixed effects models. This algorithm allows us to sample solely from standard densities, with no additional tuning being needed. We apply a stochastic search variable approach to select explanatory variables as well as to determine the structure of the random effects covariance matrix. For logistic mixed effects models prior determination of explanatory variables and random effects is no longer prerequisite since the definite structure is chosen in a data-driven manner in the course of the modeling procedure. As an illustration two real-data examples from finance and tourism studies are given. (author's abstract)
Series: Research Report Series / Department of Statistics and Mathematics
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Manikas, Vasileios. "A Bayesian Finite Mixture Model for Network-Telecommunication Data." Thesis, Stockholms universitet, Statistiska institutionen, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-146039.

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A data modeling procedure called Mixture model, is introduced beneficial to the characteristics of our data. Mixture models have been proved flexible and easy to use, a situation which can be confirmed from the majority of papers and books which have been published the last twenty years. The models are estimated using a Bayesian inference through an efficient Markov Chain Monte Carlo (MCMC) algorithm, known as Gibbs Sampling. The focus of the paper is on models for network-telecommunication lab data (not time dependent data) and on the valid predictions we can accomplish. We categorize our variables (based on their distribution) in three cases, a mixture of Normal distributions with known allocation, a mixture of Negative Binomial Distributions with known allocations and a mixture of Normal distributions with unknown allocation.
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Prosdocimi, Cecilia. "Partial exchangeability and change detection for hidden Markov models." Doctoral thesis, Università degli studi di Padova, 2010. http://hdl.handle.net/11577/3423210.

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The thesis focuses on Hidden Markov Models (HMMs). They are very popular models, because they have a more versatile structure than independent identically distributed sequences or Markov chains, but they are still tractable. It is thus of interest to look for properties of i.i.d. sequences that hold true also for HHMs, and this is the object of the thesis. In the first part we concentrate on a probabilistic problem. In particular we focus on exchangeable and partially exchangeable sequences, and we find conditions to realize them as HHMs. For a special class of binary exchangeable sequences we also give a realization algorithm. In the second part we consider the problem of detecting changes in the statistical pattern of a hidden Markov process. Adapting to HHMs the so-called cumulative sum (CUSUM) algorithm, first introduced for independent observations, we are led to the study of the CUSUM statistics with L-mixing input sequence. We establish a loss of memory property of the CUSUM statistics when there is no change, first in the easier case of a i.i.d. input sequence, (with negative expectation, and finite exponential moments of some positive order), and then, under some technical conditions, for bounded and L-mixing input sequence.
La tesi affronta lo studio dei modelli di Markov nascosti. Essi sono oggi giorno molto popolari, in quanto presentano una struttura più versatile dei processi indipendenti ed identicamente distribuiti o delle catene di Markov, ma sono tuttavia trattabili. Risulta quindi interessante cercare proprietà dei processi i.i.d. che restano valide per modelli di Markov nascosti, ed è questo l'oggetto della tesi. Nella prima parte trattiamo un problema probabilistico. In particolare ci concentriamo sui processi scambiabili e parzialmente scambiabili, trovando delle condizioni che li rendono realizzabili come processi di Markov nascosti. Per una classe particolare di processi scambiabili binari troviamo anche un algoritmo di realizzazione. Nella seconda parte affrontiamo il problema del rilevamento di un cambiamento nei parametri caratterizzanti la dinamica di un modello di Markov nascosto. Adattiamo ai modelli di Markov nascosti un algoritmo di tipo cumulative sum (CUSUM), introdotto inizialmente per osservazioni i.i.d. Questo ci porta a studiare la statistica CUSUM con processo di entrata L-mixing. Troviamo quindi una proprietà di perdita di memoria della statistica CUSUM, quando non ci sono cambiamenti nella triettoria, dapprima nel caso più elemenatare di processo di entrata i.i.d. (con media negativa e momenti esponenziali di qualche ordine finiti), e poi per processo di entrata L-mixing e limitato, sotto opportune ipotesi tecniche.
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Zhao, David Yuheng. "Model Based Speech Enhancement and Coding." Doctoral thesis, Stockholm : Kungliga Tekniska högskolan, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-4412.

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Books on the topic "Mixture Markov Model"

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Sims, Christopher A. MCMC method for Markov mixture simultaneous-equation models: A note. [Atlanta]: Federal Reserve Bank of Atlanta, 2004.

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Cheng, Russell. Finite Mixture Models. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198505044.003.0017.

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Fitting a finite mixture model when the number of components, k, is unknown can be carried out using the maximum likelihood (ML) method though it is non-standard. Two well-known Bayesian Markov chain Monte Carlo (MCMC) methods are reviewed and compared with ML: the reversible jump method and one using an approximating Dirichlet process. Another Bayesian method, to be called MAPIS, is examined that first obtains point estimates for the component parameters by the maximum a posteriori method for different k and then estimates posterior distributions, including that for k, using importance sampling. MAPIS is compared with ML and the MCMC methods. The MCMC methods produce multimodal posterior parameter distributions in overfitted models. This results in the posterior distribution of k being biased towards high k. It is shown that MAPIS does not suffer from this problem. A simple numerical example is discussed.
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Frühwirth-Schnatter, Sylvia. Finite Mixture and Markov Switching Models. Springer New York, 2010.

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Finite Mixture and Markov Switching Models. Springer New York, 2006. http://dx.doi.org/10.1007/978-0-387-35768-3.

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Finite Mixture and Markov Switching Models. Springer, 2006.

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Finite Mixture and Markov Switching Models. Springer London, Limited, 2006.

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Visser, Ingmar, and Maarten Speekenbrink. Mixture and Hidden Markov Models with R. Springer International Publishing AG, 2022.

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Visser, Ingmar, and Maarten Speekenbrink. Mixture and Hidden Markov Models with R. Springer International Publishing AG, 2022.

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Tatarinova, Tatiana V., and Alan Schumitzky. Nonlinear Mixture Models: A Bayesian Approach. Imperial College Press, 2015.

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Rodriguez, Abel, and Athanasios Kottas. Bayesian Nonparametric Mixture Models: Methods and Applications. Taylor & Francis Group, 2023.

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Book chapters on the topic "Mixture Markov Model"

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Maneejuk, Paravee, Woraphon Yamaka, and Songsak Sriboonchitta. "A Markov-Switching Model with Mixture Distribution Regimes." In Lecture Notes in Computer Science, 312–23. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-75429-1_26.

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An, Lin, Ming Li, Mohamed El Yazid Boudaren, and Wojciech Pieczynski. "Evidential Correlated Gaussian Mixture Markov Model for Pixel Labeling Problem." In Belief Functions: Theory and Applications, 203–11. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-45559-4_21.

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Blekas, Konstantinos. "A Mixture Model Based Markov Random Field for Discovering Patterns in Sequences." In Advances in Artificial Intelligence, 25–34. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11752912_5.

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Tran, Khoa Anh, Nhat Quang Vo, Tam Thi Nguyen, and Gueesang Lee. "Gaussian Mixture Model Based on Hidden Markov Random Field for Color Image Segmentation." In Lecture Notes in Electrical Engineering, 189–97. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-41671-2_25.

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Wüthrich, Mario V., and Michael Merz. "Bayesian Methods, Regularization and Expectation-Maximization." In Springer Actuarial, 207–66. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-12409-9_6.

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AbstractThis chapter summarizes some techniques that use Bayes’ theorem. These are classical Bayesian statistical models using, e.g., the Markov chain Monte Carlo (MCMC) method for model fitting. We discuss regularization of regression models such as ridge and LASSO regularization, which has a Bayesian interpretation, and we consider the Expectation-Maximization (EM) algorithm. The EM algorithm is a general purpose tool that can handle incomplete data settings. We illustrate this for different examples coming from mixture distributions, censored and truncated claims data.
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Wang, Lu, Dongxiao Zhu, Yan Li, and Ming Dong. "Poisson-Markov Mixture Model and Parallel Algorithm for Binning Massive and Heterogenous DNA Sequencing Reads." In Bioinformatics Research and Applications, 15–26. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-38782-6_2.

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Revathi Achan, E., and T. R. Swapna. "Hidden Markov Random Field and Gaussian Mixture Model Based Hidden Markov Random Field for Contour Labelling of Exudates in Diabetic Retinopathy—A Comparative Study." In Proceedings of the International Conference on ISMAC in Computational Vision and Bio-Engineering 2018 (ISMAC-CVB), 1307–17. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-00665-5_123.

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Iriawan, Nur, Kartika Fithriasari, Brodjol Sutijo Suprih Ulama, Irwan Susanto, Wahyuni Suryaningtyas, and Anindya Apriliyanti Pravitasari. "On the Markov Chain Monte Carlo Convergence Diagnostic of Bayesian Bernoulli Mixture Regression Model for Bidikmisi Scholarship Classification." In Proceedings of the Third International Conference on Computing, Mathematics and Statistics (iCMS2017), 397–403. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-7279-7_49.

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Visser, Ingmar, and Maarten Speekenbrink. "Hidden Markov Models." In Mixture and Hidden Markov Models with R, 125–72. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-01440-6_4.

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Thongkairat, Sukrit, and Chatchai Khiewngamdee. "How Does Economic Policy Uncertainty Affect Stock Market Returns: Evidence from a Markov-Switching Model with Mixture Distribution Regimes." In Credible Asset Allocation, Optimal Transport Methods, and Related Topics, 427–39. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97273-8_29.

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Conference papers on the topic "Mixture Markov Model"

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Singh, Sarangthem Ibotombi, and Smriti Kumar Sinha. "A new trust model using Hidden Markov Model based mixture of experts." In 2010 International Conference on Computer Information Systems and Industrial Management Applications (CISIM). IEEE, 2010. http://dx.doi.org/10.1109/cisim.2010.5643457.

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Wang, Xiaofeng, and Xiao-Ping Zhang. "Ice hockey shot event modeling with mixture hidden Markov model." In the 1st ACM international workshop. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1631024.1631031.

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Tse, Rina, Nisar Ahmed, and Mark Campbell. "Unified mixture-model based terrain estimation with Markov Random Fields." In 2012 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2012). IEEE, 2012. http://dx.doi.org/10.1109/mfi.2012.6343027.

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Sun, Ning, Taebo Shim, and Hernsoo Hahn. "Sonar Image Segmentation Based on Markov Gauss-Rayleigh Mixture Model." In 2008 International Workshop on Geoscience and Remote Sensing (ETT and GRS). IEEE, 2008. http://dx.doi.org/10.1109/ettandgrs.2008.380.

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Anggarwati, Febiana Putri, Azizah, and Trianingsih Eni Lestari. "Risk analysis of investment in stock market using mixture of mixture model and Bayesian Markov Chain Monte Carlo (MCMC)." In PROCEEDINGS OF THE II INTERNATIONAL SCIENTIFIC CONFERENCE ON ADVANCES IN SCIENCE, ENGINEERING AND DIGITAL EDUCATION: (ASEDU-II 2021). AIP Publishing, 2022. http://dx.doi.org/10.1063/5.0110465.

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He, Huiguang, Ke Lu, and Bin Lv. "Gaussian Mixture Model with Markov Random Field for MR Image Segmentation." In 2006 IEEE International Conference on Industrial Technology. IEEE, 2006. http://dx.doi.org/10.1109/icit.2006.372426.

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ZHANG, Lu, and Zhaoxia JING. "Non-intrusive Load Monitoring Using Factorial Hidden Markov Model Based on Gaussian Mixture Model." In 2020 IEEE Power & Energy Society General Meeting (PESGM). IEEE, 2020. http://dx.doi.org/10.1109/pesgm41954.2020.9281833.

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Zhang, Mingheng, Zhengxian Guo, Zhaoyang Liu, and Xing Wan. "Research of Driving Fatigue Detection Based on Gaussian Mixture Hidden Markov Model." In 3rd International Forum on Connected Automated Vehicle Highway System through the China Highway & Transportation Society. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2020. http://dx.doi.org/10.4271/2020-01-5158.

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Gauvain, Jean-Luc, and Chin-Hui Lee. "Bayesian learning for hidden Markov model with Gaussian mixture state observation densities." In 2nd European Conference on Speech Communication and Technology (Eurospeech 1991). ISCA: ISCA, 1991. http://dx.doi.org/10.21437/eurospeech.1991-225.

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Yang, Yalei, Hao Gao, Colin Berry, Aleksandra Radjenovic, and Dirk Husmeier. "Myocardial Perfusion Classification Using A Markov Random Field Constrained Gaussian Mixture Model." In 4th International Conference on Statistics: Theory and Applications (ICSTA'22). Avestia Publishing, 2022. http://dx.doi.org/10.11159/icsta22.146.

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