approximation can be used as a candidate density in Importance Sampling or Metropolis Hastings methods for Bayesian inference on …

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 …

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 <I>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 …

algorithm-based methods are prone to the risk of small evidence resulting in statistical significance, it is crucial to … in financial markets and credit risk models. The methods and contexts identified in the literature will be helpful for …

Carlo. This method, which is a variant of importance sampling ideas, is generally applicable to high-dimensional models …

Two Bayesian sampling schemes are outlined to estimate a K-state Markov switching model with time-varying transition …. Identification issues are addressed with random permutation sampling. In terms of efficiency, the extension to the difference in … random utility specification in combination with random permutation sampling performs best. We apply the method to estimate a …

subsets are selected using an efficient Probability Proportional-to-Size (PPS) sampling scheme, where the inclusion … applications. We propose a simple way to adaptively choose the sample size m during the MCMC to optimize sampling efficiency for a …

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 …

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 …