A class of adaptive sampling methods is introduced for efficient posterior and predictive simulation. The proposed … target and mixture is minimized. We label this approach Mixture of t by Importance Sampling and Expectation Maximization …, we introduce a permutation-augmented MitISEM approach, for importance sampling from posterior distributions in mixture …

A class of adaptive sampling methods is introduced for efficient posterior and predictive simulation. The proposed … between target and mixture is minimized. We label this approach Mixture of <I>t</I> by Importance Sampling and Expectation …, we introduce a permutation-augmented MitISEM approach, for importance sampling from posterior distributions in mixture …

A class of adaptive sampling methods is introduced for efficient posterior and predictive simulation. The proposed … between target and mixture is minimized. We label this approach Mixture of <I>t</I> by Importance Sampling and Expectation …, we introduce a permutation-augmented MitISEM approach, for importance sampling from posterior distributions in mixture …

A class of adaptive sampling methods is introduced for efficient posterior and predictive simulation. The proposed … between target and mixture is minimized. We label this approach Mixture of t by Importance Sampling and Expectation … Importance Sampling (IS) or the Metropolis-Hastings (MH) method. We also introduce three extensions of the basic MitISEM approach …

A class of adaptive sampling methods is introduced for efficient posterior and predictive simulation. The proposed … between target and mixture is minimized. We label this approach Mixture of t by Importance Sampling and Expectation …, we introduce a permutation-augmented MitISEM approach, for importance sampling from posterior distributions in mixture …

This paper presents the R-package MitISEM (mixture of t by importance sampling weighted expectation maximization) which … mixture of Student-t densities is fitted to the target using an expectation maximization (EM) algorithm where each step of the … optimization procedure is weighted using importance sampling. In the second stage this mixture density is a candidate density for …

in a Bayesian framework. This consists of a new adaptive importance sampling method for Quantile Estimation via Rapid …

used in importance sampling for model estimation, model selection and model combination. The procedure is fully automatic …