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To review some basic ideas in Monte Carlo computation and simulating random variables.

To provide a basic introduction to the Monte Carlo approach, and the generation of simulated random variables, for those who havenâ€™t seen it before.

Letâ€™s consider a general POMP model. As before, let \({y_{1:N}^*}\) be the data, and let the model consist of a latent process \(X_{0:N}\) and an observable process \(Y_{1:N}\). Then the likelihood function is \[\begin{eqnarray} {\mathscr{L}}(\theta) &=& f_{Y_{1:N}}({y_{1:N}^*}{\, ; \,}\theta) \\ &=&\int_{x_0}\cdots\int_{x_N}\! f_{X_0}(x_0{\, ; \,}\theta)\prod_{n=1}^{N}\!f_{Y_n|X_n}({y_n^*}{{\, | \,}}x_n{\, ; \,}\theta)\, f_{X_n|X_{n-1}}(x_n|x_{n-1}{\, ; \,}\theta)\, dx_0\dots dx_N. \end{eqnarray}\] i.e., computation of the likelihood requires integrating (or summing, for a discrete model) over all possible values of the unobserved latent process at each time point. This is very hard to do, in general.

Letâ€™s review, and/or learn, some *Monte Carlo* approaches for evaluating this and other difficult integrals. An excellent technical reference on Monte Carlo techniques is Robert and Casella (2004).

The basic insight of Monte Carlo methods is that we can get a numerical approximation to a challenging integral, \[ H = \int h(x)\, f(x)\, dx,\] if we can simulate (i.e., generate random draws) from the distribution with probability density function \(f\).

This insight is known as the

*fundamental theorem of Monte Carlo integration*.

**Theorem**. Let \(f(x)\) be the probability distribution function for a random variable \(X\), and let \(X_{1:J}=\{X_{j}, {j}=1,\dots,{J}\}\) be an independent and identically distributed sample of size \({J}\) from \(f\). Let \({H_{J}}\) be the sample average of \(h(X_1)\dots,h(X_{J})\), \[{H_{J}} = \frac{1}{{J}}\,\sum_{{j}=1}^{{J}}\!h(X_{j}).\] Then \({H_{J}}\) converges to \(H\) as \({J}\to\infty\) with probability 1. Less formally, we write \[{H_{J}} \approx \int\!h(x)\,f(x)\,dx.\]

**Proof**. This is the strong law of large numbers, together with the identity that \[{\mathbb{E}}[h(X)] = \int\!h(x)\,f(x)\,dx.\]

We can estimate the error in this approximation, because the empirical variance \[V_{J}= \frac{1}{{J}-1}\,\sum_{{j}=1}^{{J}}\!\big[h(X_{j})-H_{J}\big]^2\] approximates the true variance, \({\mathrm{Var}}[h(X)]={\mathbb{E}}\big[\big(h(X)-{\mathbb{E}}[h(X)]\big)^2\big]\).

The standard error on the approximation \(H_{{J}}\approx {\mathbb{E}}[h(X)]\) is therefore \[\sqrt{\frac{V_{J}}{{J}}}.\]

From the central limit theorem, the error is approximately normally distributed: \[H_{J}-{\mathbb{E}}[h(X)]\;\sim\;\mathrm{normal}\left(0,\frac{V_{J}}{{J}}\right).\]

The fundamental theorem of Monte Carlo inspires us to give further thought on how to simulate from a desired density function \(f\), which may itself be a challenging problem.

We will review simulation, but first letâ€™s consider a useful generalization of the fundamental theorem.

Sometimes it is difficult to sample directly from the distribution of \(X\).

In this case, we can often make use of

*importance sampling*, in which we generate random samples from another distribution (easier to simulate) and make the appropriate correction.Specifically, suppose we wish to compute \(\mathbb{E}[h(X)]\), where \(X\sim{f}\), but it is difficult or impossible to draw random samples from \(f\).

Suppose \(g\) is a probability distribution from which itâ€™s relatively easy to draw samples and let \(Y_{1:{J}}\) be i.i.d. random variables drawn from \(g\).

Notice that \[\mathbb{E}[h(X)] = \int\!h(x)\,f(x)\,\mathrm{d}x = \int\!h(x)\,\frac{f(x)}{g(x)}\,g(x)\, dx.\]

So, we can generalize the Monte Carlo integration theorem to give the

**Monte Carlo importance sampling theorem**, \[\mathbb{E}[h(X)] \approx \frac{1}{{J}}\,\sum_{{j}=1}^{{J}}\!h(Y_{j})\,\frac{f(Y_{j})}{g(Y_{j})}.\]We call \(w_{j}=f(Y_{j})/g(Y_{j})\) the

**importance weights**, and then we can write \[\mathbb{E}[h(X)] \approx \frac{1}{{J}}\,\sum_{{j}=1}^{{J}} w_{j}\, h(Y_{j}).\]Since \({\mathbb{E}}[w_{j}] = {\mathbb{E}}[f(Y)/g(Y)]=1\), we can modify this formula to give a

**self-normalized importance sampling**estimate, \[\mathbb{E}[h(X)] \approx \frac{\sum\!w_{j}\,h(Y_{j})}{\sum\!w_{j}}.\]The self-normalized estimate requires computation of \(w_{j}\) only up to a constant of proportionality.

The Monte Carlo variance associated with this estimate is \[\frac{\sum\!w_{j}\,(h(Y_{j})-\overline{h})^2}{\sum\!w_{j}}.\]

Obtaining accurate estimates requires some thought to the importance distribution \(g\). Specifically, if the tails of \(g\) are lighter than those of \(f\), the Monte Carlo variance will be inflated and the estimates can be unusable.

Simulation refers to the generation of random variables.

The general problem of simulation is: given a probability distribution \(f\), find a procedure that generates random draws from \(f\).

This is a very important problem in scientific computing and much thought and effort has gone into producing reliable simulators for many basic random variables.

There are two basic ways of solving this problem:

The transformation method,

The rejection method.

This method works for discrete or continuous scalar random variables.

Let \(f\) be the probability distribution function we seek to draw from (known as the

**target distribution**) and \(F\) be the corresponding cumulative distribution function, i.e., \(F(x) = \int_{-\infty}^x f(v)\, dv\).Let \(F^{-1}(u) = \inf\{x: F(x)\,\ge\,u\}\) be the inverse of \(F\).

A basic fact is that, if \(X\!\sim\!f\), then \(F(X)\!\sim\!\mathrm{uniform}(0,1)\).

Proof: If \(f(X)>0\), then \[\begin{eqnarray} {\mathbb{P}}[F(X)\,\le\,u] &=& {\mathbb{P}}[X\,<\,F^{-1}(u)] \\ &=& F\big(F^{-1}(u)\big) = u. \end{eqnarray}\]This suggests that, if we can compute \(F^{-1}\), we use the following algorithm to generate \(X\!\sim\!f\):

draw \(U \sim \mathrm{uniform}(0,1)\).

let \(X = F^{-1}(U)\).

The transformation method is very efficient in that we are guaranteed to obtain a valid \(X\) from the density \(f\) for every \(U \sim \mathrm{uniform}(0,1)\) we generate.

Sometimes, however, we cannot compute the inverse of the cumulative distribution function, as required by the transformation method.

Under such circumstances, the rejection method offers a less efficient, but more flexible, alternative.

Weâ€™ll see how and why this method works.

Let a random variable \(X\) take values in \({\mathbb{R}}^{{\mathrm{dim}(X)}}\).

Suppose that \(X\) is

**uniformly distributed**over a region \(D\subset {\mathbb{R}}^{{\mathrm{dim}(X)}}\). This means that, for any \({A}\subset{D}\), \[{\mathbb{P}}[X\in{A]}=\frac{\mathrm{area}(A)}{\mathrm{area}(D)}.\] We write \[X \sim \mathrm{uniform}(D).\]Letâ€™s suppose that we wish to simulate \(X\!\sim\!\mathrm{uniform}(D)\).

Suppose that we donâ€™t know how to directly simulate a random draw from \(D\), but we know \(D\) is a subset of some nicer region \(U\subset {\mathbb{R}}^{{\mathrm{dim}(X)}}\).

If we know how to generate \(Y\!\sim\!\mathrm{uniform}(U)\), then we can simply do so until \({Y}\in{D}\), at which point we take \(X=Y\).

Since for any \(A\subset{D}\), \[\begin{eqnarray} {\mathbb{P}}[X\in A] &=& {\mathbb{P}}[Y\in A |Y\in D] \\ &=& \frac{\mathrm{area}(A)}{\mathrm{area}(U)}\Big{/}\frac{\mathrm{area}(D)}{\mathrm{area}(U)} \\ &=& \frac{\mathrm{area}(A)}{\mathrm{area}(D)}, \end{eqnarray}\] it follows that \(Y\!\sim\!\mathrm{uniform}(D)\).

Consider an analogy to throwing darts. If the darts are thrown in such a way as to be equally likely to land anywhere in \(U\), then those that do land in \(D\) are equally likely to land anywhere in \(D\).

A useful little fact allows us to extend the rejection method from uniform distributions to arbitrary densities.

Robert and Casella (2004) refer to this fact the

*fundamental theorem of simulation*.

Let \(h\) be an arbitary positive, integrable function.

Define \[D=\{(x,u): 0{\le}u{\le}h(x)\},\] i.e., \(D\) is the graph of \(h\).

Consider the random pair \((X,U)\!\sim\!\mathrm{uniform}(D)\).

What is the marginal distribution of \(X\)? \[\int_0^{h(x)}\!\mathrm{d}u = h(x)\]

So \(h\) is the probability distribution function for \(X\)!

To carry out the rejection method, simulating from \(g\) to obtain a sample from \(f\), we take our region \(D\) to be the

**graph**of \(Mg\), i.e., \[ D = \{(x,y): 0\le y\le Mg(x),\] where \(M\) is a constant chosen so that \(Mg(x)\le f(x)\) for all \(x\).We propose points \((X,Y)\) by drawing them uniformly from the area under the graph of \(M\,g\).

We

**accept**the point \((X,Y)\) if it lies under the graph of \(f\).Then, the \(X\)-component of the \((X,Y)\) pair is distributed according to \(f\).

This suggests the following rejection method for simulating an arbitrary random variable.

Let \(f\) be the target distribution and \(g\) be another distribution function (from which it is easy to simulate) (see Figure below).

Let \(M\) be such that \(M\,g(x){\ge}f(x)\) for all \(x\).

The following procedure simulates \(X\!\sim\!f\).

draw \(Y\!\sim\!g\) and \(U\!\sim\!\mathrm{uniform}(0,M\,g(Y))\).

if \(U{\le}f(Y)\), then let \(X=Y\) else repeat step 1.

Robert, C. P., and G. Casella. 2004. Monte Carlo Statistical Methods. 2nd editions. Springer-Verlag.