Semiparametric cross entropy for rare-event simulation
نویسندگان
چکیده
منابع مشابه
Semiparametric cross entropy for rare-event simulation
The Cross Entropy method is a well-known adaptive importance sampling method for rare-event probability estimation, which requires estimating an optimal importance sampling density within a parametric class. In this article we estimate an optimal importance sampling density within a wider semiparametric class of distributions. We show that this semiparametric version of the Cross Entropy method...
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In this paper we apply the minimum cross-entropy method (MinxEnt) for estimating rare-event probabilities for the sum of i.i.d. random variables. MinxEnt is an analogy of the Maximum Entropy Principle in the sense that the objective is to minimize a relative (or cross) entropy of a target density h from an unknown density f under suitable constraints. The main idea is to use the solution to thi...
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This paper describes a new idea of finding the importance sampling density in rare events simulations: the MinxEnt method (shorthand for minimum cross-entropy). Some preliminary results show that the method might be very promising. 1 The minxent program Assume • X = (X1, . . . ,Xn) is a random vector (with values denoted by x); • h is the joint density function of X; • Sj(·) (j = 1, . . . , k) ...
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There are various importance sampling schemes to estimate rare event probabilities in Markovian systems such as Markovian reliability models and Jackson networks. In this work, we present a general state dependent importance sampling method which partitions the state space and applies the cross-entropy method to each partition. We investigate two versions of our algorithm and apply them to seve...
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ژورنال
عنوان ژورنال: Journal of Applied Probability
سال: 2016
ISSN: 0021-9002,1475-6072
DOI: 10.1017/jpr.2016.31