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Six Fundamental Methods to Generate a Random Variable


To implement many numeric simulations you need a sophisticated source of instances of random variables. The question is: how do you generate them?

The literature is full of algorithms requiring random samples as inputs or drivers (conditional random fields, Bayesian network models, particle filters and so on). The literature is also full of competing methods (pseudorandom generators, entropy sources, Gibbs samplers, Metropolis–Hastings algorithm, Markov chain Monte Carlo methods, bootstrap methods and so on). Our thesis is: this diversity is supported by only a few fundamental methods. And you are much better off thinking in terms of a few deliberately simple composable mechanisms than you would be in relying on some hugely complicated black box “brand name” technique.

We will discuss the half dozen basic methods that all of these techniques are derived from. Continue reading Six Fundamental Methods to Generate a Random Variable

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Importance Sampling

We describe briefly the powerful simulation technique known as “importance sampling.” Importance sampling is a technique that allows you to use numerical simulation to explore events that, at first look, appear too rare to be reliably approximated numerically. The correctness of importance sampling follows almost immediately from the definition of a change of density. Like most mathematical techniques, importance sampling brings in its own concerns and controls that were not obvious in the original problem. To deal with these concerns (like picking the re-weighting to use) we will largely appeal to the ideas from “A Tutorial on the Cross-Entropy Method” Pieter-Tjerk de Boer, Dirk P Kroese, Shie Mannor, and Reuven Y Rubinstein, Annals of Operations Research, 2005 vol. 134 (1) pp. 19-67. Continue reading Importance Sampling