B0635
Title: Probability based independence sampler for Bayesian quantitative learning in graphical log-linear marginal models
Authors: Claudia Tarantola - University of Pavia (Italy) [presenting]
Monia Lupparelli - University of Florence (Italy)
Ioannis Ntzoufras - AUEB (Greece)
Abstract: A fully automatic and efficient MCMC strategy is presented for quantitative learning for graphical log-linear marginal models. While the prior is expressed in terms of the marginal log-linear parameters, we build an MCMC algorithm which employs a proposal on the probability parameter space. The corresponding proposal on the marginal log-linear interactions is obtained via parameter transformations. By this strategy, we achieve to move within the desired target space. At each step we directly work with well-defined probability distributions. Moreover, we can exploit a conditional conjugate setup to build an efficient proposal on probability parameters. The proposed methodology is illustrated using a popular four-way dataset.
