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Bayesian analysis for finite Markov chains

✍ Scribed by B.R.Bhat M.N.; Badade

Publisher
Elsevier Science
Year
1999
Tongue
English
Weight
143 KB
Volume
81
Category
Article
ISSN
0378-3758

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✦ Synopsis

Bayesian approach to inference for Markov chains (MC) has many advantages over classical approach. This paper discusses how tests for one-sided and two-sided hypotheses involving two or more parameters of ΓΏnite Markov chains can be carried out. The posterior probabilities (Pvalues), Bayes factors, highest density regions (HDR) and central credible sets (CCS) and other measures are calculated for uniform and umbrella pattern prior distributions and for several functions of the two parameters in a two-state Markov chain. A numerical example is also worked out.

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