subsets are selected using an efficient Probability Proportional-to-Size (PPS) sampling scheme, where the inclusion … applications. We propose a simple way to adaptively choose the sample size m during the MCMC to optimize sampling efficiency for a …

macroeconomic models. It allows sampling from particularly challenging, high-dimensional black-box posterior distributions which may …

macroeconomic models. It allows sampling from particularly challenging, high-dimensional black-box posterior distributions which may …

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 …

Adaptive Polar Sampling (APS) is proposed as a Markov chain Monte Carlomethod for Bayesian analysis of models with ill …

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 …

. The subsample is taken via the cube method, a balanced sampling design, which is defined by the property that the sample … dass dieser nicht gespeichert werden muss. Die Stichprobe wird via cube sampling, einem balanciertem Stichprobendesign …

proposed algorithm, which is based on tempered importance sampling, adapts the model-based density forecasts to target …

This paper develops a systematic Markov Chain Monte Carlo (MCMC) framework based upon Efficient Importance Sampling … solution. EIS is a simple, generic and yet accurate Monte-Carlo integration procedure based on sampling densities which are … MCMC components such as auxiliary sampling densities, normalizing constants and starting values. The potential of this …