stepping out and multivariate sampling with hyperrectangles. In the general asymmetric case, we argue that symmetrizing the … with several sampling problems. …

based on efficient importance sampling (EIS) is detailed. Monte Carlo experiments, based on widely used diffusion processes …, evaluate its performance against an alternative importance sampling (IS) strategy, showing that EIS is at least equivalent, if …

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

induced by sampling errors. We show that the LDWB replicates first and second-order limit theory from the usual empirical …

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 …

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 …

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