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 …

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

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 …

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

risk. The key insight behind our importance sampling based approach is the sequential construction of marginal and …

simulate model parameters from the Partially Censored Posterior, and PCP-QERMit, an Importance Sampling method that is …