On the use of marginal posteriors in marginal likelihood estimation via importance sampling
Copyright policy )On the use of marginal posteriors in marginal likelihood estimation via importance sampling
We investigate the efficiency of a marginal likelihood estimator where the product of the marginal posterior distributions is used as an importance-sampling function. The approach is generally applicable to multi-block parameter vector settings, does not require additional Markov Chain Monte Carlo (MCMC) sampling and is not dependent on the type of MCMC scheme used to sample from the posterior. The proposed approach is applied to normal regression models, finite normal mixtures and longitudinal Poisson models, and leads to accurate marginal likelihood estimates.
- Athens University of Economics and Business Greece
- Durham University United Kingdom
- National and Kapodistrian University of Athens Greece
FOS: Computer and information sciences, marginal likelihood estimation, marginal posterior, Bayesian inference, Point estimation, random effect models, Statistics - Computation, importance sampling, Numerical analysis or methods applied to Markov chains, finite normal mixtures, Computational methods for problems pertaining to statistics, Rao-Blackwellization, Computation (stat.CO)
FOS: Computer and information sciences, marginal likelihood estimation, marginal posterior, Bayesian inference, Point estimation, random effect models, Statistics - Computation, importance sampling, Numerical analysis or methods applied to Markov chains, finite normal mixtures, Computational methods for problems pertaining to statistics, Rao-Blackwellization, Computation (stat.CO)
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