Bayesian Tests for Independence and Symmetry in Freund's Bivariate Exponential Model
Jang-Sik Cho, Dal-Ho Kim, Sang-Gil Kang
Abstract
Jang-Sik Cho, Dal-Ho Kim, Sang-Gil Kang
Abstract
In this paper, we consider the Bayesian hypotheses testing for independence and symmetry in Freund's bivariate exponential model. In Bayesian testing problem, we use the noninformative priors for parameters which are improper and are defined only up to arbitrary constants. And we use the recently proposed hypotheses testing criterion called the intrinsic Bayes factor. Also we derive the arithmetic and median intrinsic Bayes factors and use these results to analyze some data sets.
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In this paper, we consider the Bayesian hypotheses testing for independence and symmetry in Freund's bivariate exponential model. In Bayesian testing problem, we use the noninformative priors for parameters which are improper and are defined only up to arbitrary constants. And we use the recently proposed hypotheses testing criterion called the intrinsic Bayes factor. Also we derive the arithmetic and median intrinsic Bayes factors and use these results to analyze some data sets.
Key concepts: Bivariate analysis, Mathematics, Prior probability, Independence (probability theory), Bayes factor, Bayesian probability, Bayes' theorem, Exponential function