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As described in 1, crude (also called standard, or naive) Monte Carlo simulation is inef cient for simulating rare events Recall that crude Monte Carlo involves considering a sample of n independent copies of the random variable or process at hand, and estimating the probability of a rare event by the proportion of times the rare event occurred over that sample The resulting estimator can be considered useless when the probability of occurrence, , is very small, unless n is much larger than 1/ Indeed, if for instance = 10 9 , a frequent target in rare event applications, this would require on average a sample of size n = 109 to observe just a single occurrence of the event, and much more if we expect a reliable estimation of the mean and variance to obtain a suf ciently narrow con dence nterval Importance sampling (IS) has emerged in the literature as a powerful tool to reduce the variance of an estimator, which, in the case of rare event estimation, also means increasing the occurrence of the rare event The generic idea of IS is to change the probability laws of the system under study to sample more frequently the events that are more important for the simulation Of course, using a new distribution results in a biased estimator if no correction is applied Therefore the simulation output needs to be translated in terms of the original.

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measure; this is done by multiplication with a so-called likelihood ratio IS has received substantial theoretical attention; see inter alia [13, 12, 26] and, in the rare event context, [ 15] or the more up-to-date tutorial [16] IS is ne of the most widely used variance reduction technique in general, and for rare event estimation in particular Typical and speci c applications will be more extensively described in Part II of this book The goal of this chapter is to give an overview of the technical framework and the main underlying ideas It is organized as follows Section 22 reviews the very basic notions of IS It describes what the ideal (zero-variance) estimator looks like, and why it is, except in situations where simulation is not needed, infeasible to implement it exactly That section also provides illustrative examples and outlines some properties leading to a good IS estimator, the main message being that the zero-variance estimator has to be approximated as closely as possible In Section 23 the focus is on application of IS in the context of a Markov chain model Since every discrete-event simulation model can be seen as a Markov chain (albeit over a high-dimensional state space), this setting is very general We show how to de ne a zero-variance change of probabilities in that context It is noted that, in general, the zero-variance change of probabilities must depend on the state of the chain We compare this type of change of probabilities with a more restricted class of IS called state-independent, in which the probabilities are changed independently of the current state of the chain This type of state-independent IS originates mainly from asymptotic approximations based on large-deviations theory [3, 15, 25], and has been developed in applications areas such as queuing and nance [10, 15, 16, 19] However, in many situations, any good IS scheme must be state-dependent [3] (as state-independent IS leads to estimators with large, or even in nite, variance) Note that in computational physics (the application area from which it originates) and in reliability, IS has traditionally been state-dependent [5, 14, 16] Finally, Section 24 describes various methods used to approximate the zero-variance (ie, optimal) change of measure Some just use intuitive approximations, whereas others are based on the asymptotic behavior of the system when the events of interest become rarer and rarer (this includes methods based on large-deviations theory, and other techniques as well) Another option is to use adaptive techniques that learn (and use) approximations of the zero-variance change of measure, or optimal parameter values within a class of parameterized IS strategies: the results of completed runs can be used as inputs of strategies for the next runs, but those IS strategies can also be updated at each step of a given run [16, 22] The accuracy assessment of the resulting con dence interval, and the robustness properties of the estimator with respect to rarity, are the focus of the next chapter To avoid overlap, we keep our discussion of those aspects to a minimum here.

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