Bayesian methods for graphical log-linear marginal models have not been developed as much as traditional frequentist approaches. The likelihood function cannot be analytically expressed 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. In order to obtain an efficient algorithm, we use as proposal values draws from a Gibbs sampling on the probability parameters.

Bayesian estimation of graphical log-inear marginal models / Tarantola, C., Ntzoufras, I., Lupparelli, M.. - ELETTRONICO. - (2018), pp. 0-0. (Intervento presentato al convegno SIS 2018).

Bayesian estimation of graphical log-inear marginal models

Lupparelli M.
2018

Abstract

Bayesian methods for graphical log-linear marginal models have not been developed as much as traditional frequentist approaches. The likelihood function cannot be analytically expressed 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. In order to obtain an efficient algorithm, we use as proposal values draws from a Gibbs sampling on the probability parameters.
2018
Book of short Papers SIS 2018
SIS 2018
Tarantola, C., Ntzoufras, I., Lupparelli, M.
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1138452
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