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

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 …

these proposal densities are used in an independent Metropolis-Hastings algorithm. A particular feature of our approach is … that smoothed estimates of the states and the marginal likelihood are obtained directly as an output of the algorithm. Our …

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

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

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 …

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 …