Harman Patil (Editor)

Control variates

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The control variates method is a variance reduction technique used in Monte Carlo methods. It exploits information about the errors in estimates of known quantities to reduce the error of an estimate of an unknown quantity.

Contents

Underlying principle

Let the unknown parameter of interest be μ , and assume we have a statistic m such that the expected value of m is μ: E [ m ] = μ , i.e. m is an unbiased estimator for μ. Suppose we calculate another statistic t such that E [ t ] = τ is a known value. Then

m ⋆ = m + c ( t − τ )

is also an unbiased estimator for μ for any choice of the coefficient c . The variance of the resulting estimator m ⋆ is

Var ( m ⋆ ) = Var ( m ) + c 2 Var ( t ) + 2 c Cov ( m , t ) ;

It can be shown that choosing the optimal coefficient

c ⋆ = − Cov ( m , t ) Var ( t ) ;

minimizes the variance of m ⋆ , and that with this choice,

Var ( m ⋆ ) = Var ( m ) − [ Cov ( m , t ) ] 2 Var ( t ) = ( 1 − ρ m , t 2 ) Var ( m ) ;

where

ρ m , t = Corr ( m , t ) ;

is the correlation coefficient of m and t. The greater the value of | ρ m , t | , the greater the variance reduction achieved.

In the case that Cov ( m , t ) , Var ( t ) , and/or ρ m , t are unknown, they can be estimated across the Monte Carlo replicates. This is equivalent to solving a certain least squares system; therefore this technique is also known as regression sampling.

Example

We would like to estimate

I = ∫ 0 1 1 1 + x d x

using Monte Carlo integration. This integral is the expected value of f ( U ) , where

f ( x ) = 1 1 + x

and U follows a uniform distribution [0, 1]. Using a sample of size n denote the points in the sample as u 1 , ⋯ , u n . Then the estimate is given by

I ≈ 1 n ∑ i f ( u i ) ;

Now we introduce g ( x ) = 1 + x as a control variate with a known expected value E [ g ( U ) ] = ∫ 0 1 ( 1 + x ) d x = 3 2 and combine the two into a new estimate

I ≈ 1 n ∑ i f ( u i ) + c ( 1 n ∑ i g ( u i ) − 3 / 2 ) .

Using n = 1500 realizations and an estimated optimal coefficient c ⋆ ≈ 0.4773 we obtain the following results

The variance was significantly reduced after using the control variates technique. (The exact result is I = ln ⁡ 2 ≈ 0.69314718 .)

References

Control variates Wikipedia
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