simulation - Frank Porter January 13, 2011 Chapter 2...

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Chapter 2 Simulation Frank Porter January 13, 2011 The technology of “Monte Carlo simulation” is an important tool for un- derstanding and working with complicated probability distributions. This tech- nique is widely used in both experiment design and analysis of results. We introduce the Monte Carlo method as a means of evaluating integrals numerically. Suppose we wish to evaluate the k -dimensional integral: I = Z 1 0 ··· Z 1 0 f ( x ) d k x, (2.1) where x is a real vector in k -dimensions. A numerical estimate of this integral may be formed according to: I N = 1 N k N X ν 1 =1 N X ν k =1 f ( x ν ) , (2.2) where ν labels a vector x ν = ( x ν 1 ,...,x ν k ) with components given by: x ν i = ν i /N. (2.3) That is, we divide our k -dimensional unit hypercube integration region into N k equal pieces, evaluate f ( x ) at a point in each of these pieces, and take the average over all the pieces. As long as things are reasonably well-behaved, this will converge to the true value of the integral as we take more pieces: I = lim N →∞ I N . (2.4) A variation on this is to randomly select N points in the hypercube, and average the values of f ( x ) over all these points. In this method, we draw N random vectors r (1) ,r (2) ,...,r ( N ) from a distribution: p ( r ) = n 1 r ∈ { 0 r i 1 | i = 1 , 2 ,...,k } , 0 otherwise. (2.5) 21
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22 CHAPTER 2. SIMULATION We estimate our integral with I 0 N = 1 N N X ν =1 f ( r ( ν ) ) . (2.6) Again, if the integral is well-behaved, I 0 N will converge on the true value of I in the limit N → ∞ . There is no reason to expect that the evaluation with randomly-selected points will provide a better estimate in general for the same number of function evaluations. However, it does provide a benefit: Each of the N samplings is independent of the others – we don’t need to plan very hard what to choose for N , since taking additional samples is straightforward. Let us re-express the integral in the form of an expectation value: I 00 = I/V R = Z R f ( x ) p ( x ) d k x, (2.7) = h f i , (2.8) where R is the desired region of integration, V R = R R d k x , and p ( x ) is a uni- form sampling distribution over x R . Our approximation is thus obtained by sampling N times from p , obtaining x (1) ,...,x ( N ) and forming the sample average I 0 N = 1 N N X i =1 f ( x ( i ) ) . (2.9) Our estimate is unbiased: I 00 = h I 0 N i , (2.10) and consistent: I 00 = lim N →∞ I 0 N . (2.11) The variance of our estimator is Var( I 0 N ) = 1 N Var( f ) . (2.12) It is interesting to notice that the equal space estimate, I N , is typically biased, although it is consistent. Often, we are interested in more than the evaluation of a simple integral. There may be several integrals of interest, and we may not even know at the outset which integrals will be of greatest interest. This leads to the “simulation” aspect of the Monte Carlo method. To be more concrete, let us think in the context of an “experiment”. We regard an experiment as a sampling of variables distributed according to some differential equations (or possibly discrete equa- tions). These differential equations describe our sampling probability density function. We may model our experiment with a set of differential equations, and numerically sample from the corresponding PDFs. To get some idea of how
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simulation - Frank Porter January 13, 2011 Chapter 2...

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