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.I documenti in FLORE sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.