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 ÿnite Markov chains can be carried out. The posterior probabilities (Pvalues), Bayes factors, h
Bayesian test of homogeneity for Markov chains
✍ Scribed by Jérôme A. Dupuis
- Publisher
- Elsevier Science
- Year
- 1997
- Tongue
- English
- Weight
- 345 KB
- Volume
- 31
- Category
- Article
- ISSN
- 0167-7152
No coin nor oath required. For personal study only.
✦ Synopsis
The test we develop expresses the null hypothesis in terms of proximity of the distribution of a Markov chain (yt) to the subspace ~ of homogeneous Markov chains. The distance we use is the Kullback distance which turns out to be conceptually appropriate. Departure from the point null hypothesis allows us to formulate the question of interest in meaningful terms, but implementing this approach comes up against a scaling problem. In this paper, we propose a new approach in order to solve this scaling problem by formulating the proximity to homogeneity as a percentage of the maximum distance to ocg.
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