Open Conference Systems, 50th Scientific meeting of the Italian Statistical Society

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Power Priors for Bayesian Analysis of Graphical Models of Conditional Independence in Three Way Contingency Tables

Last modified: 2018-05-18


In this paper, we illustrate a comprehensive Bayesian analysis of graphical models of conditional independence, involving suitable choices of prior parameters, estimation, model determination, as well as the allied computational issues for three-way contingency tables. Each conditional independence model corresponds to a particular factorization of the cell probabilities and a conjugate analysis based on Dirichlet prior can be performed. Unit information interpretation priors are used as a yardstick in order to identify and interpret the effect of any other prior distribution used. The posterior distributions of the graphical models parameters, are obtained using simple Markov chain Monte Carlo (MCMC) schemes.  A real data application will be presented during the talk


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