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

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Bayesian Estimation of Graphical Log-Linear Marginal Models
Claudia Tarantola

Last modified: 2018-08-30

Abstract


Bayesian methodsfor graphical log-linear marginal models have not been developed as much as traditional frequentist approaches.The  likelihood function cannot be analyticallyexpressed in terms of  the marginal log-linear interactions, but only in terms of cell counts or probabilities. No conjugate analysis is feasible,  and MCMC methods are needed.We present a fully automatic and efficient MCMC strategy for quantitative learning based on the DAG representation of the model. While the prior is expressed in terms of the marginal log-linear interactions, the proposal is on the probability parameter space. Furthermore, in order to obtain an efficient algorithm  we use use as proposal values draws from a Gibbs sampling on the probability parameters.

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