credible sets. When approximating such contours using Monte Carlo integration methods like importance sampling or Markov chain … numerically efficient sampling. For this purpose we introduce neural networks which seem to be natural importance or candidate …

. 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 …

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

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

Adaptive radial-based direction sampling (ARDS) algorithms are specified for Bayesian analysis of models with …. After the transformations a Metropolis-Hastings method or, alternatively, an importance sampling method is applied to …

We consider an adaptive importance sampling approach to estimating the marginal likelihood, a quantity that is … importance sampling approach is that random samples can be obtained from some convenient density with little additional costs. As … method is grounded in information theory, and therefore, is in a well-defined sense optimal. We demonstrate the utility of …

quickly tuned adaptive candidate, straightforward importance sampling provides a computationally efficient estimator of the …

Algorithms, Gibbs Sampling and Metropolis-Hastings Algorithm. Network and security risk management application focus is on how …

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

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