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 …

recently developed stochastic sampling techniques and highlight their application to operational risk through the models …

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 …

We consider Bayesian inference by importance sampling when the likelihood is analytically intractable but can be … unbiasedly estimated. We refer to this procedure as importance sampling squared (IS2), as we can often estimate the likelihood … itself by importance sampling. We provide a formal justification for importance sampling when working with an estimate of the …

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