The Bayesian analysis of contingency table data using the bayesloglin R package

2016/12/16 by Matthew Friedlander, Friedlander, Matthew
Computer Science · Mathematics · #Bayesian Methods and Mixture Models #Bayesian Modeling and Causal Inference #Computation (stat.CO) #FOS: Computer and information sciences #Markov Chains and Monte Carlo Methods

paper · pdf · doi:10.48550/arxiv.1612.05501

openalex publication_date 2016/12/16 · openalex created_date 2017/02/10 · openalex updated_date 2026/07/28

Abstract

For log-linear analysis, the hyper Dirichlet conjugate prior is available to work in the Bayesian paradigm. With this prior, the MC3 algorithm allows for exploration of the space of models to try to find those with the highest posterior probability. Once top models have been identified, a block Gibbs sampler can be constructed to sample from the posterior distribution and to estimate parameters of interest. Our aim in this paper, is to introduce the bayesloglin R package \citepR which contains functions to carry out these tasks.

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