Giù Marcer 2e2a4eacf9 ex-5: complete writing

2020-07-05 11:35:03 +02:00

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Exercize 5

The following integral must be evaluated:


  I = \int\limits_0^1 dx \, e^x

\begin{figure} \hypertarget{fig:exp}{% \centering \begin{tikzpicture} \definecolor{cyclamen}{RGB}{146, 24, 43} % Integral \filldraw [cyclamen!15!white, domain=0:5, variable=\x] (0,0) -- plot({\x},{exp(\x/5)}) -- (5,0) -- cycle; \draw [cyclamen] (5,0) -- (5,2.7182818); \node [below] at (5,0) {1}; % Axis \draw [thick, <-] (0,4) -- (0,0); \draw [thick, ->] (-2,0) -- (7,0); \node [below right] at (7,0) {$x$}; \node [above left] at (0,4) {$e^{x}$}; % Plot \draw [domain=-2:7, smooth, variable=\x, cyclamen, ultra thick] plot ({\x},{exp(\x/5)}); \end{tikzpicture} \caption{Plot of the integral to be evaluated.} } \end{figure}

whose exact value is 1.7182818285...

The three most popular Monte Carlo (MC) methods where applied: plain MC, Miser and Vegas. Besides this popularity fact, these three method were chosen for being the only ones implemented in the GSL library.

Plain Monte Carlo

When the integral I over a $n-$dimensional space \Omega of volume V of a function f must be evaluated, that is:


  I = \int\limits_{\Omega} dx \, f(x)
  \with V = \int\limits_{\Omega} dx

the simplest MC method approach is to sample N points x_i evenly distributed in V and approx I as:


  I \sim I_N = \frac{V}{N} \sum_{i=1}^N f(x_i) = V \cdot \langle f \rangle

with I_N \rightarrow I for N \rightarrow + \infty for the law of large numbers. Hence, the sample variance can be extimated by the sample variance:


  \sigma^2_f = \frac{1}{N - 1} \sum_{i = 1}^N \left( f(x_i) - \langle f
  \rangle \right)^2 \et \sigma^2_I = \frac{V^2}{N^2} \sum_{i = 1}^N
  \sigma^2_f = \frac{V^2}{N} \sigma^2_f

Thus, the error decreases as 1/\sqrt{N}.
Unlike in deterministic methods, the estimate of the error is not a strict error bound: random sampling may not uncover all the important features of the integrand and this can result in an underestimate of the error.

In this case, f(x) = e^{x} and \Omega = [0,1].

Since the proximity of I_N to I is related to N, the accuracy of the method is determined by how many points are generated, namely how many function calls are exectuted when the method is implemented. In @tbl:MC, the obtained results and errors \sigma are shown. The estimated integrals for different numbers of calls are compared to the expected value I and the difference 'diff' between them is given.
As can be seen, the MC method tends to underestimate the error for scarse function calls. As previously stated, the higher the number of function calls, the better the estimation of I. A further observation regards the fact that, even with 50'000'000 calls, the I^{\text{oss}} still differs from I at the fifth decimal digit.

               500'000 calls     5'000'000 calls    50'000'000 calls

I^{\text{oss}} 1.7166435813 1.7181231109 1.7183387184

\sigma 0.0006955691 0.0002200309 0.0000695809

diff 0.0016382472 0.0001587176 0.0000568899

Table: MC results with different numbers of function calls. {#tbl:MC}

Stratified sampling

In statistics, stratified sampling is a method of sampling from a population partitioned into subpopulations. Stratification, indeed, is the process of dividing the primary sample into subgroups (strata) before sampling random within each stratum.
Given the mean \bar{x}_i and variance {\sigma^2_x}_i of an entity $x$ sorted with simple random sampling in each strata, such as:


  \bar{x}_i = \frac{1}{n_i} \sum_j x_j


  \sigma_i^2 = \frac{1}{n_i - 1} \sum_j \left( x_j - \bar{x}_i \right)^2
  \thus
  {\sigma^2_x}_i = \frac{1}{n_i^2} \sum_j \sigma_i^2 = \frac{\sigma_i^2}{n_i}

where:

j runs over the points x_j sampled in the i^{\text{th}} stratum
n_i is the number of points sorted in it
\sigma_i^2 is the variance associated with the j^{\text{th}} point

then the mean \bar{x} and variance \sigma_x^2 estimated with stratified sampling for the whole population are:


  \bar{x} = \frac{1}{N} \sum_i N_i \bar{x}_i \et
  \sigma_x^2 = \sum_i \left( \frac{N_i}{N} \right)^2 {\sigma_x}^2_i
             = \sum_i \left( \frac{N_i}{N} \right)^2 \frac{\sigma^2_i}{n_i}

where i runs over the strata, N_i is the weight of the $i^{\text{th}}$ stratum and N is the sum of all strata weights.

In practical terms, it can produce a weighted mean that has less variability than the arithmetic mean of a simple random sample of the whole population. In fact, if measurements within strata have lower standard deviation, the final result will have a smaller error in estimation with respect to the one otherwise obtained with simple sampling.
For this reason, stratified sampling is used as a method of variance reduction when MC methods are used to estimate population statistics from a known population.

MISER

The MISER technique aims to reduce the integration error through the use of recursive stratified sampling.
As stated before, according to the law of large numbers, for a large number of extracted points, the estimation of the integral I can be computed as:


  I= V \cdot \langle f \rangle

Since V is known (in this case, V = 1), it is sufficient to estimate \langle f \rangle.

Consider two disjoint regions a and b, such that a \cup b = \Omega, in which n_a and n_b points were uniformely sampled. Given the Monte Carlo estimates of the means \langle f \rangle_a and \langle f \rangle_b of those points and their variances \sigma_a^2 and \sigma_b^2, if the weights $N_a$ and N_b of \langle f \rangle_a and \langle f \rangle_b are chosen unitary, then the variance \sigma^2 of the combined estimate \langle f \rangle:


  \langle f \rangle = \frac{1}{2} \left( \langle f \rangle_a
                    + \langle f \rangle_b \right)

is given by:


  \sigma^2 = \frac{\sigma_a^2}{4n_a} + \frac{\sigma_b^2}{4n_b}

It can be shown that this variance is minimized by distributing the points such that:


  \frac{n_a}{n_a + n_b} = \frac{\sigma_a}{\sigma_a + \sigma_b}

Hence, the smallest error estimate is obtained by allocating sample points in proportion to the standard deviation of the function in each sub-region.
The whole integral estimate and its variance are therefore given by:


  I = V \cdot \langle f \rangle \et \sigma_I^2 = V^2 \cdot \sigma^2

When implemented, MISER is in fact a recursive method. With a given step, all the possible bisections are tested and the one which minimizes the combined variance of the two sub-regions is selected. The variance in the sub-regions is estimated with a fraction of the total number of available points. The remaining sample points are allocated to the sub-regions using the formula for n_a and n_b, once the variances are computed.
The same procedure is then repeated recursively for each of the two half-spaces from the best bisection. At each recursion step, the integral and the error are estimated using a plain Monte Carlo algorithm. After a given number of calls, the final individual values and their error estimates are then combined upwards to give an overall result and an estimate of its error.

Results for this particular sample are shown in @tbl:MISER.

               500'000 calls     5'000'000 calls    50'000'000 calls

I^{\text{oss}} 1.7182850738 1.7182819143 1.7182818221

\sigma 0.0000021829 0.0000001024 0.0000000049

diff 0.0000032453 0.0000000858 000000000064

Table: MISER results with different numbers of function calls. Be careful: while in @tbl:MC the number of function calls stands for the number of total sampled poins, in this case it stands for the times each section is divided into subsections. {#tbl:MISER}

This time the error, altough it lies always in the same order of magnitude of diff, seems to seesaw around the correct value, which is much more closer to the expected one.

Importance sampling

In statistics, importance sampling is a method which samples points from the probability distribution f itself, so that the points cluster in the regions that make the largest contribution to the integral.

Remind that I = V \cdot \langle f \rangle and therefore only $\langle f \rangle$ must be estimated. Then, consider a sample of n points {$x_i$} generated according to a probability distribition function P which gives thereby the following expected value:


  E [x, P] = \frac{1}{n} \sum_i x_i

with variance:


  \sigma^2 [E, P] = \frac{\sigma^2 [x, P]}{n}
  \with \sigma^2 [x, P] = \frac{1}{n -1} \sum_i \left( x_i - E [x, P] \right)^2

where i runs over the sample.
In the case of plain MC, \langle f \rangle is estimated as the expected value of points {$f(x_i)$} sorted with P (x_i) = 1 \quad \forall i, since they are evenly distributed in \Omega. The idea is to sample points from a different distribution to lower the variance of E[x, P], which results in lowering \sigma^2 [x, P]. This is accomplished by choosing a random variable y and defining a new probability P^{(y)} in order to satisfy:


  E [x, P] = E \left[ \frac{x}{y}, P^{(y)} \right]

which is to say:


  I = \int \limits_{\Omega} dx f(x) = 
      \int \limits_{\Omega} dx \, \frac{f(x)}{g(x)} \, g(x)=
      \int \limits_{\Omega} dx \, w(x) \, g(x)

where E \, \longleftrightarrow \, I and:


  \begin{cases}
    f(x) \, \longleftrightarrow \, x              \\
    1  \, \longleftrightarrow \, P
  \end{cases}
  \et
  \begin{cases}
    w(x) \, \longleftrightarrow \, \frac{x}{y}    \\
    g(x) \, \longleftrightarrow \, y = P^{(y)}
  \end{cases}

Where the symbol \longleftrightarrow points out the connection between the variables. This new estimate is better than the former if:


  \sigma^2 \left[ \frac{x}{y}, P^{(y)} \right] < \sigma^2 [x, P]

The best variable y would be:


  y^{\star} = \frac{x}{E [x, P]} \, \longleftrightarrow \, \frac{f(x)}{I}
  \thus \frac{x}{y^{\star}} = E [x, P]

and even a single sample under P^{(y^{\star})} would be sufficient to give its value. Obviously, it is not possible to take exactly this choice, since $E [x, P]$ is not given a priori.
However, this gives an insight into what importance sampling does. In fact, given that:


  E [x, P] = \int \limits_{a = - \infty}^{a = + \infty}
             a P(x \in [a, a + da])

the best probability change P^{(y^{\star})} redistributes the law of x so that its samples frequencies are sorted directly according to their weights in E[x, P], namely:


  P^{(y^{\star})}(x \in [a, a + da]) = \frac{1}{E [x, P]} a P (x \in [a, a + da])

In conclusion, since certain values of x have more impact on E [x, P] than others, these "important" values must be emphasized by sampling them more frequently. As a consequence, the estimator variance will be reduced.

VEGAS

The VEGAS algorithm is based on importance sampling. It aims to reduce the integration error by concentrating points in the regions that make the largest contribution to the integral.

As stated before, in practice it is impossible to sample points from the best distribution P^{(y^{\star})}: only a good approximation can be achieved. In GSL, the VEGAS algorithm approximates the distribution by histogramming the function f in different subregions. Each histogram is used to define a sampling distribution for the next pass, which consists in doing the same thing recorsively: this procedure converges asymptotically to the desired distribution. It follows that a better estimation is achieved with a greater number of function calls.
The integration uses a fixed number of function calls. The result and its error estimate are based on a weighted average of independent samples, as for MISER.
For this particular sample, results are shown in @tbl:VEGAS.

               500'000 calls     5'000'000 calls    50'000'000 calls

I^{\text{oss}} 1.7182818354 1.7182818289 1.7182818285

\sigma 0.0000000137 0.0000000004 0.0000000000

diff 0.0000000069 0.0000000004 0.0000000000

Table: VEGAS results with different numbers of function calls. {#tbl:VEGAS}

This time, the error estimation is notably close to diff for each number of function calls, meaning that the estimation of both the integral and its error turn out to be very accurate, much more than the ones obtained with both plain Monte Carlo method and stratified sampling.

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