Bayesian estimation of the Gaussian mixture GARCH model
Computational Statistics & Data Analysis
Marginal likelihoods for non-Gaussian models using auxiliary mixture sampling
Computational Statistics & Data Analysis
On marginal likelihood computation in change-point models
Computational Statistics & Data Analysis
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
The marginal likelihood of dynamic mixture models
Computational Statistics & Data Analysis
Classification of molecular sequence data using Bayesian phylogenetic mixture models
Computational Statistics & Data Analysis
| Hi-index | 0.03 |
Strategic choices for efficient and accurate evaluation of marginal likelihoods by means of Monte Carlo simulation methods are studied for the case of highly non-elliptical posterior distributions. A comparative analysis is presented of possible advantages and limitations of different simulation techniques; of possible choices of candidate distributions and choices of target or warped target distributions; and finally of numerical standard errors. The importance of a robust and flexible estimation strategy is demonstrated where the complete posterior distribution is explored. Given an appropriately yet quickly tuned adaptive candidate, straightforward importance sampling provides a computationally efficient estimator of the marginal likelihood (and a reliable and easily computed corresponding numerical standard error) in the cases investigated, which include a non-linear regression model and a mixture GARCH model. Warping the posterior density can lead to a further gain in efficiency, but it is more important that the posterior kernel be appropriately wrapped by the candidate distribution than that it is warped.