کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
416394 681366 2012 17 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Estimating discrete Markov models from various incomplete data schemes
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نظریه محاسباتی و ریاضیات
پیش نمایش صفحه اول مقاله
Estimating discrete Markov models from various incomplete data schemes
چکیده انگلیسی

The parameters of a discrete stationary Markov model are transition probabilities between states. Traditionally, data consist in sequences of observed states for a given number of individuals over the whole observation period. In such a case, the estimation of transition probabilities is straightforwardly made by counting one-step moves from a given state to another. In many real-life problems, however, the inference is much more difficult as state sequences are not fully observed, namely the state of each individual is known only for some given values of the time variable. A review of the problem is given, focusing on Monte Carlo Markov Chain (MCMC) algorithms to perform Bayesian inference and evaluate posterior distributions of the transition probabilities in this missing-data framework. Leaning on the dependence between the rows of the transition matrix, an adaptive MCMC mechanism accelerating the classical Metropolis–Hastings algorithm is then proposed and empirically studied.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computational Statistics & Data Analysis - Volume 56, Issue 9, September 2012, Pages 2609–2625
نویسندگان
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