Journal ArticleDOI
Weighted Uniform Sampling — a Monte Carlo Technique for Reducing Variance
M. J. D. Powell,J. Swann +1 more
Reads0
Chats0
About:
This article is published in Ima Journal of Applied Mathematics.The article was published on 1966-09-01. It has received 66 citations till now. The article focuses on the topics: Monte Carlo integration & Control variates.read more
Citations
More filters
Proceedings ArticleDOI
The lumigraph
TL;DR: A new method for capturing the complete appearance of both synthetic and real world objects and scenes, representing this information, and then using this representation to render images of the object from new camera positions.
Proceedings Article
Data-efficient off-policy policy evaluation for reinforcement learning
Philip S. Thomas,Emma Brunskill +1 more
TL;DR: A new way of predicting the performance of a reinforcement learning policy given historical data that may have been generated by a different policy, based on an extension of the doubly robust estimator and a new way to mix between model based estimates and importance sampling based estimates.
Journal ArticleDOI
Methods for Approximating Integrals in Statistics with Special Emphasis on Bayesian Integration Problems
Michael Evans,Tim B. Swartz +1 more
TL;DR: A survey of the major techniques and approaches available for the numerical approximation of integrals in statistics can be found in this article, where the authors classify these into five broad categories; namely, asymptotic methods, im- portance sampling, adaptive importance sampling, multiple quadrature and Markov chain methods.
Journal ArticleDOI
Numerical Evaluation of Multiple Integrals
TL;DR: A survey of the main methods for numerical evaluation of multiple integrals can be found in this article, where the Monte Carlo method and its generalizations are discussed, as well as number-theoretical methods, based essentially on the ideas of Diophantine approximation and equidistribution modulo 1; functional analysis approach, in which the quadrature error is regarded as a linear functional and one attempts to minimize its norm.
Journal ArticleDOI
Smoothness and dimension reduction in Quasi-Monte Carlo methods
TL;DR: Modified Monte Carlo methods are developed, using smoothing and dimension reduction, so that the convergence rate of nearly O (N^-^1) is regained and the effective dimension of the integration domain is drastically reduced.