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Markov Chain Monte Carlo: an introduction for epidemiologists

Ghassan Hamra, Richard MacLehose, David Richardson
2013 International Journal of Epidemiology  
Markov Chain Monte Carlo (MCMC) methods are increasingly popular among epidemiologists.  ...  standard maximum-likelihood estimation (MLE).  ...  Acknowledgements We would like to thank Dr Stephen Cole for helpful discussion that led to the development of this work. MCMC FOR EPIDEMIOLOGISTS  ... 
doi:10.1093/ije/dyt043 pmid:23569196 pmcid:PMC3619958 fatcat:mplbn2qjbfbi5iwpbv2734hb44

Bayesian Logistic Regression Modelling via Markov Chain Monte Carlo Algorithm

Henry De-Graft Acquah
2013 Journal of Social and Development Sciences  
This paper introduces Bayesian analysis and demonstrates its application to parameter estimation of the logistic regression via Markov Chain Monte Carlo (MCMC) algorithm.  ...  It is concluded that Bayesian Markov Chain Monte Carlo algorithm offers an alternative framework for estimating the logistic regression model.  ...  The Markov Chain Monte Carlo algorithm is also presented with emphasis on the Metropolis Hastings algorithm. Data used in the study is also described.  ... 
doi:10.22610/jsds.v4i4.751 fatcat:2ygymmqkz5b57nkdrt4zjgr7zq

Stochastic gradient estimation strategies for Markov random fields

Laurent Younes, Ali Mohammad-Djafari
1998 Bayesian Inference for Inverse Problems  
This communication presents new results about convergence of stochastic gradient algorithms for maximum likelihood estimation of Markov random fields.  ...  We first present theoretical results dealing with the convergence of a generalized Robbins-Monro procedure.  ...  SAMPLING FROM MARKOV RANDOM FIELDS It is not in our intent to give here a precise description of the sampling algorithms which can be used for Markov random fields.  ... 
doi:10.1117/12.323811 fatcat:e7mgq3cfe5gbdd3pp4ufrmlzsm

Page 2044 of Mathematical Reviews Vol. , Issue 2002C [page]

2002 Mathematical Reviews  
by Markov chain Monte Carlo stochastic approximation.  ...  A number of spe- cial processes are described and the use of Markov chain Monte Carlo techniques to generate sample point patterns is described.  ... 

Combining Monte Carlo and Mean-Field-Like Methods for Inference in Hidden Markov Random Fields

Florence Forbes, Gersende Fort
2007 IEEE Transactions on Image Processing  
Her research activities include Bayesian image analysis, Markov models, and hidden structure models.  ...  She joined the IS2 research team at INRIA Rhône-Alpes, France, in 1998, where she has been Head of the MISTIS team since 2003.  ...  In practice, we choose and sample the Markov random field by using Markov chain Monte Carlo samplers.  ... 
doi:10.1109/tip.2006.891045 pmid:17357740 fatcat:lf4fjrtb75f77htjmmouddilpq

Page 136 of Biometrics Vol. 58, Issue 1 [page]

2002 Biometrics  
Practical Markov chain Monte Carlo. Statistical Science 7, 473-511. Gilks, W. R., Richardson, S., and Spoegelhalter, D. J. (1996). Markov Chain Monte Carlo in Practice.  ...  A Bayesian analysis of kriging. Technometrics 35, 403-410. Hastings, W. K. (1970). Monte Carlo sampling methods using Markov chain and their applications. Biometrika 57, 97- 109.  ... 

Page 4970 of Mathematical Reviews Vol. , Issue 2001G [page]

2001 Mathematical Reviews  
2001g:62021 62 in the 1990s did statisticians (re)discover Markov chain Monte Carlo (McMC), and soon they realized how it makes feasible sta- tistical Bayesian and likelihood computations which one otherwise  ...  authors is to develop a numerical algorithm to compute the maximum likelihood estimator of (p;,---, px) sub- ject to some order constraints on In p;,---,In py.  ... 

Markov chain Monte Carlo techniques applied to parton distribution functions determination: Proof of concept

Yémalin Gabin Gbedo, Mariane Mangin-Brinet
2017 Physical Review D  
We present a new procedure to determine Parton Distribution Functions (PDFs), based on Markov Chain Monte Carlo (MCMC) methods.  ...  A first feasibility study is performed and presented, which indicates that Markov Chain Monte Carlo can successfully be applied to the extraction of PDFs and of their uncertainties.  ...  PRINCIPLE OF MARKOV CHAIN MONTE CARLO A.  ... 
doi:10.1103/physrevd.96.014015 fatcat:zq3g6p3ianbyblkpl7bfwkn4pa

Bayesian Restoration of Digital Images Employing Markov Chain Monte Carlo a Review [article]

K. P. N. Murthy, M. Janani, B. Shenbga Priya
2006 arXiv   pre-print
, restoration of an image through Posterior maximization, statistical estimation of a true image from Posterior ensembles, Markov Chain Monte Carlo methods and cluster algorithms.  ...  A review of Bayesian restoration of digital images based on Monte Carlo techniques is presented.  ...  Sridhar for carrying out independent simulations for testing preliminary versions of the algorithms reported in this review. KPN is thankful to S.  ... 
arXiv:cs/0504037v2 fatcat:khd53nxsezghhnijnxjrs3cdti

Review on Parameter Estimation in HMRF [article]

Namjoon Suh
2017 arXiv   pre-print
Following section deals with an issue on parameters estimation process of Gaussian Hidden Markov Random Field using MAP estimation and EM algorithm, and also discusses problems, found through several experiments  ...  This is a technical report which explores the estimation methodologies on hyper-parameters in Markov Random Field and Gaussian Hidden Markov Random Field.  ...  Parameter estimation of Hidden Markov Spatio-Temporal Random Field.  ... 
arXiv:1711.07561v1 fatcat:p5rsdwlcgnbdzergr57j3etbgu

Gamma Markov Random Fields for Audio Source Modeling

O. Dikmen, A.T. Cemgil
2010 IEEE Transactions on Audio, Speech, and Language Processing  
In this paper, we describe a class of prior models called Gamma Markov random fields (GMRFs) to model the sparsity and the local dependency of the energies (i.e., variances) of time-frequency expansion  ...  The marginal likelihood of the model is not available because of the intractable normalizing constant of GMRFs.  ...  During the inference of latent variables using Monte Carlo methods, this expected complete log likelihood can be evaluated using Monte Carlo samples drawn from the posterior of the latent variables where  ... 
doi:10.1109/tasl.2009.2031778 fatcat:valhskewfvd7xjrn2i67m2vpvu

A multiresolution EM algorithm for unsupervised image classification

J.-M. Laferte, F. Heitz, P. Perez
1996 Proceedings of 13th International Conference on Pattern Recognition  
This algorithm is an e cient alternative to expensive or approximate EM algorithms associated with Markov Random Fields.  ...  We take bene t from a causal Markov model de ned on a quadtree to derive a multiresolution EM algorithm for unsupervised image classi cation.  ...  In this paper we consider a di erent modeling framework (Causal Markov Random Fields de ned on a quadtree) which enables a maximum likelihood estimation of the model parameters to be computed in an unsupervised  ... 
doi:10.1109/icpr.1996.547196 dblp:conf/icpr/LaferteHP96 fatcat:hopw2dstqvayfnmccgjjkg54va

MARKOV CHAIN MONTE CARLO METHODS FOR MODELING THE SPATIAL PATTERN OF DISEASE SPREAD IN BELL PEPPER

Jonathan M. Graham
1996 Conference on Applied Statistics in Agriculture  
This Markov chain Monte Carlo maximum likelihood (MCMCML) procedure provides a competitor to the usual pseudolikelihood estimation method often used for modeling discrete lattice data.  ...  This Markov chain Monte Carlo maximum likelihood (MCMCML) procedure provides a competitor to the usual pseudolikelihood estimation method often used for modeling discrete lattice data.  ...  Jean Ristaino for the use of her data.  ... 
doi:10.4148/2475-7772.1321 fatcat:ouwh76s4bffrdj5wqutfxjhkam

Computing the Cramer–Rao Bound of Markov Random Field Parameters: Application to the Ising and the Potts Models

Marcelo Pereyra, Nicolas Dobigeon, Hadj Batatia, Jean-Yves Tourneret
2014 IEEE Signal Processing Letters  
Index Terms-Cramer-Rao bound, intractable distributions, Markov random fields, Monte Carlo algorithms.  ...  This letter considers the problem of computing the Cramer-Rao bound for the parameters of a Markov random field.  ...  This sampler belongs to the class of Markov chain Monte Carlo (MCMC) algorithms, which are interesting for MRFs because they not require to know .  ... 
doi:10.1109/lsp.2013.2290329 fatcat:vtwb6ltumrah3pqjn6qugiyunm

Computing the Cramer-Rao bound of Markov random field parameters: Application to the Ising and the Potts models [article]

Marcelo Pereyra, Nicolas Dobigeon, Hadj Batatia, Jean-Yves Tourneret
2013 arXiv   pre-print
This report considers the problem of computing the Cramer-Rao bound for the parameters of a Markov random field.  ...  The proposed methodology is successfully applied on the Ising and the Potts models.% where it is used to assess the performance of three state-of-the art estimators of the parameter of these Markov random  ...  This sampler belongs to the class of Markov chain Monte Carlo algorithms, which are interesting for MRFs because they not require to know C(θ).  ... 
arXiv:1206.3985v3 fatcat:twxxcjclnndczhulqmkqsgz37q
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