importance sampling or the independence chain Metropolis-Hastings algorithm for posterior analysis. A comparative analysis is … appropriately yet quickly tuned candidate, straightforward importance sampling provides the most efficient estimator of the marginal …

This paper presents the R-package MitISEM (mixture of t by importance sampling weighted expectation maximization) which … optimization procedure is weighted using importance sampling. In the second stage this mixture density is a candidate density for … efficient and robust application of importance sampling or the Metropolis-Hastings (MH) method to estimate properties of the …

Adaptive Polar Sampling is proposed as an algorithm where random drawings aredirectly generated from the target …

Adaptive Polar Sampling (APS) is proposed as a Markov chain Monte Carlomethod for Bayesian analysis of models with ill …

quickly tuned adaptive candidate, straightforward importance sampling provides a computationally efficient estimator of the …

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 …