On improved estimation for importance sampling

On improved estimation for importance sampling

The standard estimator used in conjunction with importance sampling in Monte Carlo integration is unbiased but inefficient. An alternative estimator is discussed, based on the idea of a difference estimator, which is asymptotically optimal. The improved estimator uses the importance weight as a control variate, as previously studied by Hesterberg (Ph.D. Dissertation, Stanford University (1988);...

Balanced Importance Sampling Estimation

In this paper we analyze a particular issue of estimation, namely the estimation of the expected value of an unknown function for a given distribution, with the samples drawn from other distributions. A motivation of this problem comes from machine learning. In reinforcement learning, an intelligent agent that learns to make decisions in an unknown environment encounters the problem of judging ...

Quantile Estimation with Adaptive Importance Sampling

MTTF Estimation using importance sampling on Markov models

Very complex systems occur nowadays quite frequently in many technological areas and they are often required to comply with high dependability standards. To study their availability and reliability characteristics, Markovian models are commonly used. Due to the size and complexity of the systems, and due to the rarity of system failures, both analytical solutions and \crude" simulation can be i...

Improved importance sampling distribution for rate constant calculation.

An efficient method to compute the thermal rate constant for rare events within the correlation function C(t) approach is presented. This method, which is based on a modification of the sampling function used to evaluate the dynamical correlation function C(t), can be applied to high-dimensional systems having a rough energy landscape without previous knowledge on the transition states location...