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 …

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 … Importance Sampling (IS) or the Metropolis-Hastings (MH) method. We also introduce three extensions of the basic MitISEM approach …

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

approximation can be used as a candidate density in Importance Sampling or Metropolis Hastings methods for Bayesian inference on … model parameters and probabilities. The package provides also an extended MitISEM algorithm, 'sequential MitISEM', which … predictive likelihoods. We illustrate the MitISEM algorithm using three canonical statistical and econometric models that are …

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

-rejection sampling step within DMC. This Acceptance-Rejection within Direct Monte Carlo (ARDMC) method has the attractive property that …-known 'Metropolis-Hastings within Gibbs' sampling in the sense that one 'more difficult' step is used within an 'easier' simulation …

regressors. An Approximate DMC (ADMC) approach is introduced thatmakes use of the proposed Hybrid Mixture Sampling (HMS) method …, which facilitates Metropolis-Hastings (MH) or Importance Sampling from a proper marginalposterior density with highly non …

the tedious task of tuning a MCMC sampling algorithm. The usage of the package is shown in an empirical application to …