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Bayesian Analysis of Discrete Survival Data with a Hidden Markov Chain
Author(s) -
Kozumi Hideo
Publication year - 2000
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.0006-341x.2000.01002.x
Subject(s) - markov chain monte carlo , markov chain , bayesian probability , computer science , variable order markov model , markov chain mixing time , hazard , markov model , markov property , mathematics , econometrics , algorithm , statistics , artificial intelligence , machine learning , chemistry , organic chemistry
Summary. This paper considers the discrete survival data from a Bayesian point of view. A sequence of the baseline hazard functions, which plays an important role in the discrete hazard function, is modeled with a hidden Markov chain. It is explained how the resultant model is implemented via Markov chain Monte Carlo methods. The model is illustrated by an application of real data.