ex-5: review
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@ -31,9 +31,9 @@ techniques.
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whose exact value is 1.7182818285...
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The three most popular Monte Carlo (MC) methods where applied: plain MC, Miser
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and Vegas. Besides this popularity fact, these three method were chosen for
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being implemented in the GSL libraries `gsl_monte_plain`, `gsl_monte_miser` and
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`gsl_monte_vegas` respectively.
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and Vegas. Besides being commonly used, these were chosen for also being
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implemented in the GSL libraries `gsl_monte_plain`, `gsl_monte_miser` and
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`gsl_monte_vegas`, respectively.
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## Plain Monte Carlo
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@ -45,39 +45,42 @@ $$
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\with V = \int\limits_{\Omega} dx
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$$
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the simplest MC method approach is to sample $N$ points $x_i$ evenly distributed
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in $V$ and approx $I$ as:
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the simplest MC method approach is to sample $N$ points $x_i$ in $V$ and
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approximate $I$ as:
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$$
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I \sim I_N = \frac{V}{N} \sum_{i=1}^N f(x_i) = V \cdot \langle f \rangle
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I \approx I_N = \frac{V}{N} \sum_{i=1}^N f(x_i) = V \cdot \avg{f}
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$$
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with $I_N \rightarrow I$ for $N \rightarrow + \infty$ because of the law of
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large numbers, whereas the sample variance can be estimated as:
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If $x_i$ are uniformly distributed $I_N \rightarrow I$ for $N \rightarrow +
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\infty$ by the law of large numbers, whereas the sample variance can be
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estimated as:
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$$
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\sigma^2_f = \frac{1}{N - 1} \sum_{i = 1}^N \left( f(x_i) - \langle f
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\rangle \right)^2 \et \sigma^2_I = \frac{V^2}{N^2} \sum_{i = 1}^N
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\sigma^2_f = \frac{V^2}{N} \sigma^2_f
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\sigma^2_f = \frac{1}{N - 1}
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\sum_{i = 1}^N \left( f(x_i) - \avg{f} \right)^2
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\et
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\sigma^2_I = \frac{V^2}{N^2} \sum_{i = 1}^N
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\sigma^2_f = \frac{V^2}{N} \sigma^2_f
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$$
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Thus, the error decreases as $1/\sqrt{N}$.
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Unlike in deterministic methods, the estimate of the error is not a strict error
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bound: random sampling may not uncover all the important features of the
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integrand and this can result in an underestimate of the error.
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Unlike in deterministic methods, the error estimate is not a strict bound:
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random sampling may not cover all the important features of the integrand and
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this can result in an underestimation of the error.
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In this case $f(x) = e^{x}$ and $\Omega = [0,1]$, hence $V = 1$.
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Since the proximity of $I_N$ to $I$ is related to $N$, the accuracy of the
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Since the distance from $I$ of $I_N$ is related to $N$, the accuracy of the
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method lies in how many points are generated, namely how many function calls
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are executed when the iterative method is implemented. In @fig:MC and
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@fig:MI, results obtained with the plain MC method are shown in red. In
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@tbl:MC, some of them are listed: the estimated integrals $I^{\text{oss}}$ are
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compared to the expected value $I$ and the differences diff between them are
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given.
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are executed when the iterative method is implemented. In @fig:plain-mc-iter
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and @fig:miser-iter, results obtained with the plain MC method are shown in
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red. In @tbl:plain-mc-res, some of them are listed: the estimated integrals
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$I^{\text{oss}}$ are compared to the expected value $I$ and the differences
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between them are given.
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{#fig:MC}
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around the correct value.](images/5-MC_MC.pdf){#fig:plain-mc-iter}
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---------------------------------------------------------------------------
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calls $I^{\text{oss}}$ $\sigma$ diff
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@ -91,12 +94,12 @@ calls $I^{\text{oss}}$ $\sigma$ diff
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Table: Some MC results with three different numbers of function calls.
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Differences between computed and exact values are given in
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diff. {#tbl:MC}
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diff. {#tbl:plain-mc-res}
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As can be seen, $\sigma$ is always of the same order of magnitude of diff,
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except for very low numbers of function calls. Even with \num{5e7} calls,
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$I^{\text{oss}}$ still differs from $I$ at the fifth decimal digit, meaning that
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this method shows a really slow convergence. In fact, since the $\sigma$s
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$I^{\text{oss}}$ still differs from $I$ at the fifth decimal place, meaning
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that this method shows a really slow convergence. In fact, since the $\sigma$
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dependence on the number $C$ of function calls is confirmed:
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$$
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\begin{cases}
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@ -110,46 +113,48 @@ $$
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\frac{\sigma_1}{\sigma_2} = 9.9965508 \sim 10 = \sqrt{\frac{C_2}{C_1}}
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$$
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if an error of $\sim 1^{-n}$ is required, a number $\propto 10^{2n}$ of
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function calls should be executed, meaning that for $\sigma \sim 1^{-10}
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\rightarrow C = \num{1e20}$, which would be impractical.
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For an error of $10^{-n}$, a number $\propto 10^{2n}$ of function calls is
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needed. To compute an integral within double precision,
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an impossibly large number of $\sigma \sim 10^{32}$ calls is needed,
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which makes this method unpractical for high-precision applications.
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## Stratified sampling
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In statistics, stratified sampling is a method of sampling from a population
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partitioned into subpopulations. Stratification, indeed, is the process of
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dividing the primary sample into subgroups (strata) before sampling random
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dividing the primary sample into subgroups (strata) before sampling
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within each stratum.
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Given the mean $\bar{x}_i$ and variance ${\sigma^2_x}_i$ of an entity $x$
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sorted with simple random sampling in the $i^{\text{th}}$ strata, such as:
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Given a sample $\{x_j\}_i$ of the $i$-th strata, its mean $\bar{x}_i$ and
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variance ${\sigma^2_x}_i$, are given by
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$$
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\bar{x}_i = \frac{1}{n_i} \sum_j x_j
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$$
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and:
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and from:
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$$
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\sigma_i^2 = \frac{1}{n_i - 1} \sum_j \left( x_j - \bar{x}_i \right)^2
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\thus
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{\sigma^2_x}_i = \frac{1}{n_i^2} \sum_j \sigma_i^2 = \frac{\sigma_i^2}{n_i}
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$$
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where:
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- $j$ runs over the points $x_j$ sampled in the $i^{\text{th}}$ stratum,
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- $n_i$ is the number of points sorted in it,
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- $\sigma_i^2$ is the variance associated with the $j^{\text{th}}$ point.
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- $j$ runs over the points $x_j$ of the sample
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- $n_i$ is the size of the sample
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- $\sigma_i^2$ is the variance associated to every point of the $i$-th
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stratum.
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then, the mean $\bar{x}$ and variance $\sigma_x^2$ estimated with stratified
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sampling for the whole population are:
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An estimation of the mean $\bar{x}$ and variance $\sigma_x^2$ for the whole
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population are then given by the stratified sampling as follows:
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$$
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\bar{x} = \frac{1}{N} \sum_i N_i \bar{x}_i \et
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\sigma_x^2 = \sum_i \left( \frac{N_i}{N} \right)^2 {\sigma_x}^2_i
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= \sum_i \left( \frac{N_i}{N} \right)^2 \frac{\sigma^2_i}{n_i}
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$$
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where:
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where $i$ runs over the strata, $N_i$ is the weight of the $i^{\text{th}}$
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stratum and $N$ is the sum of all strata weights.
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- $i$ runs over the strata,
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- $N_i$ is the weight of the $i$-th stratum
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- $N$ is the sum of all strata weights.
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In practical terms, it can produce a weighted mean that has less variability
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than the arithmetic mean of a simple random sample of the whole population. In
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@ -158,34 +163,31 @@ result will have a smaller error in estimation with respect to the one otherwise
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obtained with simple sampling.
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For this reason, stratified sampling is used as a method of variance reduction
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when MC methods are used to estimate population statistics from a known
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population [@ridder17].
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population. For examples, see [@ridder17].
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### MISER
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The MISER technique aims to reduce the integration error through the use of
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The MISER technique aims at reducing the integration error through the use of
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recursive stratified sampling.
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As stated before, according to the law of large numbers, for a large number of
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extracted points, the estimation of the integral $I$ can be computed as:
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$$
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I= V \cdot \langle f \rangle
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I= V \cdot \avg{f}
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$$
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Since $V$ is known (in this case, $V = 1$), it is sufficient to estimate
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$\langle f \rangle$.
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$\avg{f}$.
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Consider two disjoint regions $a$ and $b$, such that $a \cup b = \Omega$, in
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which $n_a$ and $n_b$ points are respectively uniformly sampled. Given the
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Monte Carlo estimates of the means $\langle f \rangle_a$ and $\langle f
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\rangle_b$ of those points and their variances $\sigma_a^2$ and $\sigma_b^2$, if
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the weights $N_a$ and $N_b$ of $\langle f \rangle_a$ and $\langle f \rangle_b$
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are chosen unitary, then the variance $\sigma^2$ of the combined estimate
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$\langle f \rangle$:
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Monte Carlo estimates of the means $\avg{f}_a$ and $\avg{f}_b$ of those points
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and their variances $\sigma_a^2$ and $\sigma_b^2$, if the weights $N_a$ and
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$N_b$ of $\avg{f}_a$ and $\avg{f}_b$ are chosen unitary, then the variance
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$\sigma^2$ of the combined estimate $\avg{f}$:
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$$
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\langle f \rangle = \frac{1}{2} \left( \langle f \rangle_a
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+ \langle f \rangle_b \right)
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\avg{f} = \frac{1}{2} \left( \avg{f}_a + \avg{f}_b \right)
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$$
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is given by:
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$$
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\sigma^2 = \frac{\sigma_a^2}{4n_a} + \frac{\sigma_b^2}{4n_b}
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@ -201,182 +203,170 @@ Hence, the smallest error estimate is obtained by allocating sample points in
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proportion to the standard deviation of the function in each sub-region.
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The whole integral estimate and its variance are therefore given by:
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$$
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I = V \cdot \langle f \rangle \et \sigma_I^2 = V^2 \cdot \sigma^2
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I = V \cdot \avg{f} \et \sigma_I^2 = V^2 \cdot \sigma^2
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$$
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When implemented, MISER is in fact a recursive method. First, all the possible
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bisections of $\Omega$ are tested and the one which minimizes the combined
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variance of the two sub-regions is selected. In order to speed up the
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algorithm, the variance in the sub-regions is estimated with a fraction of the
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total number of available points (function calls) which is default set to 0.1.
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total number of available points (function calls), in GSL it default to 0.1.
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The remaining points are allocated to the sub-regions using the formula for
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$n_a$ and $n_b$, once the variances are computed.
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The same procedure is then repeated recursively for each of the two half-spaces
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from the best bisection. When less than 32*16 (default option) function calls
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are available in a single section, the integral and the error in this section
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are estimated using the plain Monte Carlo algorithm.
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This procedure is then repeated recursively for each of the two half-regions
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from the best bisection. When the allocated calls for a region running out
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(less than 512 in GSL), the method falls back to a plain Monte Carlo.
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The final individual values and their error estimates are then combined upwards
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to give an overall result and an estimate of its error [@sayah19].
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{#fig:MI}
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{#fig:miser-iter}
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Results for this particular sample are shown in black in @fig:MI and some of
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them are listed in @tbl:MI. Except for the first very little number of calls,
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the improvement with respect to the Plain MC technique (in red) is appreciable.
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Results for this particular sample are shown in black in @fig:miser-iter and
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some of them are listed in @tbl:miser-res. Except for the first very little
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number of calls, the improvement with respect to the Plain MC technique (in
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red) is appreciable.
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---------------------------------------------------------------------------
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calls $I^{\text{oss}}$ $\sigma$ diff
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------------------ ------------------ ------------------ ------------------
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\num{5e5} 1.7182850738 0.0000021829 0.0000032453
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--------------------------------------------------------------------
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calls $I^{\text{oss}}$ $\sigma$ diff
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----------- ------------------ ------------------ ------------------
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\num{5e5} 1.7182850738 0.0000021829 0.0000032453
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\num{5e6} 1.7182819143 0.0000001024 0.0000000858
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\num{5e6} 1.7182819143 0.0000001024 0.0000000858
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\num{5e7} 1.7182818221 0.0000000049 0.0000000064
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---------------------------------------------------------------------------
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\num{5e7} 1.7182818221 0.0000000049 0.0000000064
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--------------------------------------------------------------------
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Table: MISER results with different numbers of function calls. Differences
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between computed and exact values are given in diff. {#tbl:MI}
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between computed and exact values are given in diff. {#tbl:miser-res}
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This time the convergence is much faster: already with 500'000 number of points,
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the correct results differs from the computed one at the fifth decimal digit.
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Once again, the error lies always in the same order of magnitude of diff.
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The convergence is much faster than a plain MC: at 500'000 function calls, the
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estimate agrees with the exact integral to the fifth decimal place. Once again,
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the standard deviation and the difference share the same magnitude.
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## Importance sampling
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In statistics, importance sampling is a method which samples points from the
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probability distribution $f$ itself, so that the points cluster in the regions
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that make the largest contribution to the integral.
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In Monte Carlo methods, importance sampling is a technique which samples points
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from distribution whose shape is close to the integrand $f$ itself. In this way
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the points cluster in the regions that make the largest contribution to the
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integral $\int f(x)dx$ and consequently decrease the variance.
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Remind that $I = V \cdot \langle f \rangle$ and therefore only $\langle f
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\rangle$ is to be estimated. Consider a sample of $n$ points {$x_i$} generated
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according to a probability distribution function $P$ which gives thereby the
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following expected value:
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In a plain MC the points are sampled uniformly, so their probability
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density is given by
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$$
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E [x, P] = \frac{1}{n} \sum_i x_i
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g(x) = \frac{1}{V} \quad \forall x \in \Omega
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$$
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with variance:
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and the integral can be written as
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$$
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\sigma^2 [E, P] = \frac{\sigma^2 [x, P]}{n}
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\with \sigma^2 [x, P] = \frac{1}{n -1} \sum_i \left( x_i - E [x, P] \right)^2
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I = \int_\Omega dx f(x) = V \int_\Omega f(x) \frac{1}{V}dx
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\approx V \avg{f}
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$$
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where $i$ runs over the sample.
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In the case of plain MC, $\langle f \rangle$ is estimated as the expected
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value of points {$f(x_i)$} sorted with $P (x_i) = 1 \quad \forall i$, since they
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are evenly distributed in $\Omega$. The idea is to sample points from a
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different distribution to lower the variance of $E[x, P]$, which results in
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lowering $\sigma^2 [x, P]$. This is accomplished by choosing a random variable
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$y$ and defining a new probability $P^{(y)}$ in order to satisfy:
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More generally, consider a distribution $h(x)$ and similarly do
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$$
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E [x, P] = E \left[ \frac{x}{y}, P^{(y)} \right]
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I
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= \int_\Omega dx f(x)
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= \int_\Omega dx \, \frac{f(x)}{h(x)} \, h(x)
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= \Exp \left[ \frac{f}{h}, h \right]
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$$
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where $\Exp[X, h]$ is the expected value of $X$ wrt $h$.
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Also note that $h$ has to vanish outside $\Omega$ for this to hold.
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which is to say:
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As anticipated, to reduce the variance $h$ must be close to $f$.
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Assuming they are proportional, $h(x) = \alpha |f(x)|$, it follows
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that:
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$$
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I = \int \limits_{\Omega} dx f(x) =
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\int \limits_{\Omega} dx \, \frac{f(x)}{g(x)} \, g(x)=
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\int \limits_{\Omega} dx \, w(x) \, g(x)
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$$
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where $E \, \longleftrightarrow \, I$ and:
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$$
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\begin{cases}
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f(x) \, \longleftrightarrow \, x \\
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1 \, \longleftrightarrow \, P
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\end{cases}
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\Exp \left[ \frac{f}{h}, h \right] = \frac{1}{\alpha}
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\et
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\begin{cases}
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g(x) \, \longleftrightarrow \, y = P^{(y)} \\
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w(x) = \frac{f(x)}{g(x)} \, \longleftrightarrow \, \frac{x}{y}
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\end{cases}
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\Var \left[ \frac{f}{h}, h \right] = 0
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$$
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Where the symbol $\longleftrightarrow$ points out the connection between the
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variables. The best variable $y$ would be:
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For the expected value to give the original $I$, the proportionality constant
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must be taken to be $I^{-1}$, meaning:
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$$
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y = \frac{x}{E [x, P]} \, \longleftrightarrow \, \frac{f(x)}{I}
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\thus \frac{x}{y} = E [x, P]
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h(z) = \frac{1}{I}\, |f(z)|
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$$
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and even a single sample under $P^{(y)}$ would be sufficient to give its value:
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it can be shown that under this probability distribution the variance vanishes.
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Obviously, it is not possible to take exactly this choice, since $E [x, P]$ is
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not given a priori. However, this gives an insight into what importance sampling
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does. In fact, given that:
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$$
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E [x, P] = \int \limits_{a = - \infty}^{a = + \infty}
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da \, a P(x \in [a, a + da])
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$$
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The sampling from this $h$ would produce a perfect result with zero variance.
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Of course, this is nonsense: if $I$ is known in advance, there would be no need
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to do a Monte Carlo integration to begin with. Nonetheless, this example serves
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to prove how variance reduction is achieved by sampling from an approximation
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of the integrand.
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the best probability $P^{(y)}$ redistributes the law of $x$ so that its samples
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frequencies are sorted directly according to their weights in $E[x, P]$, namely:
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$$
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P^{(y}(x \in [a, a + da]) = \frac{1}{E [x, P]} a P (x \in [a, a + da])
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$$
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In conclusion, since certain values of $x$ have more impact on $E [x, P]$ than
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others, these "important" values must be emphasized by sampling them more
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In conclusion, since certain values of $x$ have more impact on $\Exp[f/h, h]$
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than others, these "important" values must be emphasized by sampling them more
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frequently. As a consequence, the estimator variance will be reduced.
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### VEGAS
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The VEGAS algorithm [@lepage78] of G. P. Lepage is based on importance
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sampling. As stated before, it is in practice impossible to sample points from
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the best distribution $h(x)$: only a good approximation can be achieved. The
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VEGAS algorithm attempts this by building a histogram of the function $f$ in
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different subregions with an iterative method, namely:
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The VEGAS algorithm of Lepage is based on importance sampling. It aims to
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reduce the integration error by concentrating points in the regions that make
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the largest contribution to the integral.
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- a fixed number of points (function calls) is generated uniformly in the
|
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whole region;
|
||||
|
||||
As stated before, in practice it is impossible to sample points from the best
|
||||
distribution $P^{(y)}$: only a good approximation can be achieved. In GSL, the
|
||||
VEGAS algorithm approximates the distribution by histogramming the function $f$
|
||||
in different subregions with a iterative method [@lepage78], namely:
|
||||
|
||||
- a fixed number of points (function calls) is evenly generated in the whole
|
||||
region;
|
||||
- the volume $V$ is divided into $N$ intervals with width $\Delta x_i =
|
||||
- the volume $V$ is divided into $N$ intervals of width $\Delta x_i =
|
||||
\Delta x \, \forall \, i$, where $N$ is limited by the computer storage
|
||||
space available and must be held constant from iteration to iteration.
|
||||
Default $N = 50$;
|
||||
(In GSL this default to $N = 50$);
|
||||
|
||||
- each interval is then divided into $m_i + 1$ subintervals, where:
|
||||
$$
|
||||
m_i = K \frac{\bar{f}_i \Delta x_i}{\sum_j \bar{f}_j \Delta x_j}
|
||||
$$
|
||||
$$
|
||||
m_i = K \frac{\bar{f}_i \Delta x_i}{\sum_j \bar{f}_j \Delta x_j}
|
||||
$$
|
||||
|
||||
where $j$ runs over all the intervals and $\bar{f}_i$ is the average value
|
||||
of $f$ in the interval. Hence, $m_i$ is therefore a measure of the
|
||||
"importance" of the interval with respect to the others: the higher
|
||||
$\bar{f}_i$, the higher $m_i$. The stiffness $K$ is default set to 1.5;
|
||||
$\bar{f}_i$, the higher $m_i$. The constant $K$ is called stiffness.
|
||||
It is defaults 1.5 in GSL;
|
||||
|
||||
- as it is desirable to restore the number of intervals to its original value
|
||||
$N$, groups of the new intervals must be amalgamated into larger intervals,
|
||||
the number of subintervals in each group being constant. The net effect is
|
||||
$N$, groups of the new intervals must be merged into larger intervals, the
|
||||
number of subintervals in each group being constant. The net effect is
|
||||
to alter the intervals sizes, while keeping the total number constant, so
|
||||
that the smallest intervals occur where $f$ is largest;
|
||||
- the new grid is used and further refined in subsequent iterations until the
|
||||
optimal grid has been obtained. By default, the number of iterations is set
|
||||
to 5.
|
||||
|
||||
At the end, a cumulative estimate of the integral $I$ and its error $\sigma_I$
|
||||
are made based on weighted average:
|
||||
- the function is integrated with a plain MC method in each interval
|
||||
and the sum of the integrals is taken as the $j$-th estimate of $I$. Its
|
||||
error is given the sum of the variances in each interval.
|
||||
|
||||
- the new grid is used and further refined in subsequent iterations.
|
||||
By default, the number of iterations 5 in GSL.
|
||||
|
||||
The final estimate of the integral $I$ and its error
|
||||
$\sigma_I$ are made based on weighted average:
|
||||
$$
|
||||
I = \sigma_I^2 \sum_i \frac{I_i}{\sigma_i^2}
|
||||
\avg{I} = \sigma_I^2 \sum_i \frac{I_i}{\sigma_i^2}
|
||||
\with
|
||||
\sigma_I^2 = \left( \sum_i \frac{1}{\sigma_i^2} \right)^{-1}
|
||||
\frac{1}{\sigma_I^2} = \sum_i \frac{1}{\sigma_i^2}
|
||||
$$
|
||||
where $I_i$ and $\sigma_i$ are are the integral and standard deviation
|
||||
estimated in each iteration.
|
||||
|
||||
The reliability of the result is asserted by a chi-squared per degree of
|
||||
freedom $\chi_r^2$, which should be close to 1 for a good estimation. At a
|
||||
given iteration $i$, the $\chi^2_i$ is computed as follows:
|
||||
$$
|
||||
\chi^2_i = \sum_{j \le i}
|
||||
\frac{(I_j - \avg{I})^2}{\sigma_j^2}
|
||||
$$
|
||||
|
||||
where $I_i$ and $\sigma_i$ are are the integral and standard deviation
|
||||
estimated in each interval $\Delta x_i$ of the last iteration using the plain
|
||||
MC technique.
|
||||
For the results to be reliable, the chi-squared per degree of freedom
|
||||
$\chi_r^2$ must be consistent with 1. While performing the eiterations, if
|
||||
the $\chi_r^2$ value exceed 1.5, the cycle breaks.
|
||||
While performing the iterations, if the value of $\chi_r^2$ exceed 1.5, the
|
||||
routine stops since is not making progress.
|
||||
|
||||
Clearly, once again a better estimation is achieved with a greater number of
|
||||
function calls. For this particular sample, the most accurate results are shown
|
||||
in @fig:MI_VE and some of them are listed in @tbl:VEGAS.
|
||||
Clearly, a better estimation is achieved with a greater number of function
|
||||
calls. For this particular sample, the most accurate results are shown in
|
||||
@fig:vegas-iter and some of them are listed in @tbl:vegas-res.
|
||||
|
||||
----------------------------------------------------------------------------------------------
|
||||
calls $I^{\text{oss}}$ $\sigma$ diff $\chi_r^2$
|
||||
@ -389,19 +379,19 @@ calls $I^{\text{oss}}$ $\sigma$ diff
|
||||
----------------------------------------------------------------------------------------------
|
||||
|
||||
Table: Some VEGAS results with different numbers of
|
||||
function calls. {#tbl:VEGAS}
|
||||
function calls. {#tbl:vegas-res}
|
||||
|
||||
As can be appreciated in @fig:MI_VE, the VEGAS algorithm manages to compute
|
||||
the integral value in a most accurate way with respect to MISER. The $\chi_r^2$
|
||||
turns out to be close enough to 1 to guarantee a good estimation of $I$,
|
||||
goodness which is also confirmed by the very small difference between estimation
|
||||
and exact value, as shown in @tbl:VEGAS: with a number of \num{5e7} of function
|
||||
calls, the difference is smaller than \num{1e-10}.
|
||||
As can be appreciated in @fig:vegas-iter, the VEGAS algorithm manages to compute the
|
||||
integral value more accurately compared to MISER. The $\chi_r^2$ turns out to
|
||||
be close enough to 1 to guarantee a good estimation of $I$, goodness which is
|
||||
also confirmed by the very small difference shown in @tbl:vegas-res.
|
||||
In fact, with a number of \num{5e7} function calls, the difference is
|
||||
smaller than \num{1e-10}.
|
||||
|
||||
{#fig:MI_VE}
|
||||
results.](images/5-MC_MI_VE.pdf){#fig:vegas-iter}
|
||||
|
||||
In conclusion, between a plain Monte Carlo technique, stratified sampling and
|
||||
importance sampling, the last one turned out to be the most powerful mean to
|
||||
importance sampling, the last turned out to be the most powerful mean to
|
||||
obtain a good estimation of the integrand.
|
||||
|
||||
Loading…
Reference in New Issue
Block a user