A WEIGHT-BOUNDED IMPORTANCE SAMPLING METHOD FOR VARIANCE REDUCTION
نویسندگان
چکیده
منابع مشابه
Variance Reduction in SGD by Distributed Importance Sampling
Humans are able to accelerate their learning by selecting training materials that are the most informative and at the appropriate level of difficulty. We propose a framework for distributing deep learning in which one set of workers search for the most informative examples in parallel while a single worker updates the model on examples selected by importance sampling. This leads the model to up...
متن کاملVariance Reduction Techniques of Importance Sampling Monte Carlo Methods for Pricing Options
In this paper we discuss the importance sampling Monte Carlo methods for pricing options. The classical importance sampling method is used to eliminate the variance caused by the linear part of the logarithmic function of payoff. The variance caused by the quadratic part is reduced by stratified sampling. We eliminate both kinds of variances just by importance sampling. The corresponding space ...
متن کاملImportance Sampling for Markov Chains: Asymptotics for the Variance
In this paper, we apply the Perron-Frobenius theory for non-negative matrices to the analysis of variance asymptotics for simulations of finite state Markov chain to which importance sampling is applied. The results show that we can typically expect the variance to grow (at least) exponentially rapidly in the length of the time horizon simulated. The exponential rate constant is determined by t...
متن کاملVariance reduction in large graph sampling
The norm of practice in estimating graph properties is to use uniform random node (RN) samples whenever possible. Many graphs are large and scale-free, inducing large degree variance and estimator variance. This paper shows that random edge (RE) sampling and the corresponding harmonic mean estimator for average degree can reduce the estimation variance significantly. First, we demonstrate that ...
متن کاملZero-Variance Importance Sampling Estimators for Markov Process Expectations
We study the structure of zero-variance importance sampling estimators for expectations of functionals of Markov processes. For a class of expectations that can be characterized as solutions to linear systems, we show that a zerovariance estimator can be constructed by using an importance distribution that preserves the Markovian nature of the underlying process. This suggests that good practic...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: International Journal for Uncertainty Quantification
سال: 2019
ISSN: 2152-5080
DOI: 10.1615/int.j.uncertaintyquantification.2019029511