A MCMC algorithm to fit a general exchangeable model |
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Authors: | Jim Albert |
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Affiliation: | Department of MAthematics and Statistics , Bowling Green State University , Bowling Green, OH, 43403 |
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Abstract: | Consider the exchangeable Bayesian hierarchical model where observations yi are independently distributed from sampling densities with unknown means, the means µi, are a random sample from a distribution g, and the parameters of g are assigned a known distribution h. A simple algorithm is presented for summarizing the posterior distribution based on Gibbs sampling and the Metropolis algorithm. The software program Matlab is used to implement the algorithm and provide a graphical output analysis. An binomial example is used to illustrate the flexibility of modeling possible using this algorithm. Methods of model checking and extensions to hierarchical regression modeling are discussed. |
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Keywords: | hierarchical model Gibbs sampler Metropolis algorithm logistic model output analysis |
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