Greedy Importance Sampling
نویسنده
چکیده
I present a simple variation of importance sampling that explicitly searches for important regions in the target distribution. I prove that the technique yields unbiased estimates, and show empirically it can reduce the variance of standard Monte Carlo estimators. This is achieved by concentrating samples in more significant regions of the sample space.
منابع مشابه
Regularized Greedy Importance Sampling
Greedy importance sampling is an unbiased estimation technique that reduces the variance of standard importance sampling by explicitly searching for modes in the estimation objective. Previous work has demonstrated the feasibility of implementing this method and proved that the technique is unbiased in both discrete and continuous domains. In this paper we present a reformulation of greedy impo...
متن کاملMonte Carlo inference via greedy importance sampling
We present a new method for conducting Monte Carlo inference in graphical models which combines explicit search with generalized importance sampling. The idea is to reduce the variance of importance sampling by searching for significant points in the target distribution. We prove that it is possible to introduce search and still maintain unbiasedness. We then demonstrate our procedure on a few ...
متن کاملGreedy importance sampling: A new Monte Carlo inference method
This paper presents a new Monte Carlo inference method that is a simple variation of importance sampling. It is well known that importance sampling fails when the proposal distribution concentrates samples in low density regions of the target distribution, which leads to high variance in the estimates. The method we propose attempts to mitigate this difficulty by explicitly searching for high d...
متن کاملA Permutation Genetic Algorithm For Variable Ordering In Learning Bayesian Networks From Data
Greedy score-based algorithms for learning the structure of Bayesian networks may produce very different models depending on the order in which variables are scored. These models often vary significantly in quality when applied to inference. Unfortunately, finding the optimal ordering of inputs entails search through the permutation space of variables. Furthermore, in real-world applications of...
متن کاملA stochastic analysis of greedy routing in a spatially-dependent sensor network
For a sensor network, a tractable spatially-dependent node deployment model is presented with the property that the density is inversely proportional to the sink distance. A stochastic model is formulated to examine message advancements under greedy routing in such a sensor network. The aim of this work is to demonstrate that an inhomogeneous Poisson process can be used to model a sensor networ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 1999