Truncated Importance Sampling
نویسندگان
چکیده
منابع مشابه
Truncated importance sampling
Importance sampling is a fundamental Monte Carlo technique. It involves generating a sample from a proposal distribution in order to estimate some property of a target distribution. Importance sampling can be highly sensitive to the choice of proposal distribution, and fails if the proposal distribution does not sufficiently well approximate the target. Procedures which involve truncation of la...
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Reinforcement Learning (RL) is considered here as an adaptation technique of neural controllers of machines. The goal is to make Actor-Critic algorithms require less agent-environment interaction to obtain policies of the same quality, at the cost of additional background computations. We propose to achieve this goal in the spirit of experience replay. An estimation method of improvement direct...
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There are some experiences that researcher come across quite number of time for very large networks in the initial samples such that they cannot finish the sampling procedure. Two solutions have been proposed and used by marine biologists which we discuss in this article: i) Adaptive cluster sampling based on order statistics with a stopping rule, ii) Restricted adaptive cluster sampling. Until...
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There are some experiences that researcher come across quite number of time for very large networks in the initial samples such that they cannot finish the sampling procedure. Two solutions have been proposed and used by marine biologists which we discuss in this article: i) Adaptive cluster sampling based on order statistics with a stopping rule, ii) Restricted adaptive cluster sampling. Until...
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We describe an application of using a change of sampling density to get easier access to rare events during numeric simulations (this is called importance sampling). Our emphasis is on the derivation of the change of density instead of the algorithmic details. We work a small example to make the technique concrete.
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ژورنال
عنوان ژورنال: Journal of Computational and Graphical Statistics
سال: 2008
ISSN: 1061-8600,1537-2715
DOI: 10.1198/106186008x320456