Fast simulation of rare events in queueing and reliability models
ACM Transactions on Modeling and Computer Simulation (TOMACS)
A maximum entropy approach to natural language processing
Computational Linguistics
Modelling extremal events: for insurance and finance
Modelling extremal events: for insurance and finance
Image identification and estimation using the maximum entropy principle
Pattern Recognition Letters
Simulating heavy tailed processes using delayed hazard rate twisting
ACM Transactions on Modeling and Computer Simulation (TOMACS)
The Cross Entropy Method: A Unified Approach To Combinatorial Optimization, Monte-carlo Simulation (Information Science and Statistics)
Operations Research
Introduction to Rare Event Simulation
Introduction to Rare Event Simulation
Resource allocation under uncertainty using the maximum entropy principle
IEEE Transactions on Information Theory
A fast estimation of SRAM failure rate using probability collectives
Proceedings of the 2012 ACM international symposium on International Symposium on Physical Design
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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 this optimization program as the simulation density in importance sampling. We shall see that some eXisting importance sampling methods can be cast in a MinXEnt program, such as the large deviations approach for light tails and the hazard rate twisting for heavy tails. As an eXtension, we shall consider a correlated version of this hazard rate twisted solution which gives better simulation results. The sample generation is based on a Gibbs sampler algorithm.