Monte Carlo Integration - Importance Sampling

In summary, Importance Sampling Monte Carlo integration is a method for estimating integrals by using a non-uniform probability distribution. This method can be implemented by using the Monte Carlo importance sampling formula and can be extended to multidimensional integrals by integrating one dimension at a time. However, it is important to scale the range of integration appropriately to get accurate results.
  • #1
trelek2
88
0
Hi!

I'm trying to write an algorithm that performes Importance Sampling Monte Carlo integration.

I found some info about it, but I have trouble understanding how it actually can be implemented.

Let f(x) be the function we want to integrate and p(x) the non-uniform probability distribution function which is normalized and is in the same shape as f(x). p(x) returns values in the range of the interval we want to integrate. The monte carlo importance sampling formula is the following:
[tex] <f>=\int_{0}^{1}f(x)dx= \int_{0}^{1}p(x) \frac{f(x)}{p(x)}dx [/tex]
So I thought that the implementation is the following
[tex]<f>= \frac{1}{N} \sum_{i=1}^{N} \frac{f(x _{i} )}{p(x _{i} )} [/tex]
and the estimated error will be given by:
[tex] ( \frac{1}{N} \sum_{i=1}^{N} \frac{f(x _{i} )*(f(x _{i} )}{p(x _{i} )}) -<f> ^{2}[/tex]

The problem is that this works only for integrals in the range 0 to 1.
What should I do if I want to integrate over the range from a to b?

And how are these formulas modified when dealing with a multidimentional integral. If my understanding of importance sampling is already wrong please correct me!
 
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  • #2
The problem is that this works only for integrals in the range 0 to 1.
What should I do if I want to integrate over the range from a to b?
The simplest method is simply to scale (assuming a and b finite) a<x<b.
Let y = (x-a)/(b-a)

And how are these formulas modified when dealing with a multidimentional integral. If my understanding of importance sampling is already wrong please correct me!

Your understanding is correct as far as it goes. For multidimensional integrals, it is easier to do the integration one dimension at a time.
 

Related to Monte Carlo Integration - Importance Sampling

What is Monte Carlo Integration?

Monte Carlo Integration is a numerical method for estimating the value of a definite integral using random sampling. It involves generating random points within the integration limits and using these points to approximate the integral value.

What is Importance Sampling in Monte Carlo Integration?

Importance Sampling is a variance reduction technique used in Monte Carlo Integration. It involves selecting points from a different probability distribution than the one being integrated, which can lead to a more accurate estimate of the integral.

Why is Importance Sampling used in Monte Carlo Integration?

Importance Sampling can improve the accuracy and efficiency of Monte Carlo Integration by reducing the variance of the estimates. This is especially useful for integrands with high variance or when the integration limits are large.

How does Importance Sampling work in Monte Carlo Integration?

In Importance Sampling, a new probability distribution is chosen that has a similar shape to the integrand. Random points are then generated from this distribution and used to estimate the integral. The weights of these points are adjusted to account for the difference in the chosen distribution and the actual integrand.

What are the benefits of using Monte Carlo Integration with Importance Sampling?

Using Monte Carlo Integration with Importance Sampling can lead to more accurate and efficient estimates of integrals compared to traditional Monte Carlo methods. It also allows for the integration of complex and high-dimensional functions that may be difficult to evaluate using other numerical methods.

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