Computational Physics Resources: Monte Carlo Integration & Importance Sampling

written by
Spencer Wheaton

This website contains a set of 2 simulations and accompanying worksheets that introduce the techniques of sample mean, hit and miss and importance sampling Monte Carlo integration.

Please note that this resource requires
at least version 1.6 of
Java (JRE).

Hit and Miss and Sample Mean Methods Worksheet
A worksheet to accompany the EJS simulation MCIntegration No1 1DHitMissSampleMean.jar download 151kb .pdf
Last Modified: March 8, 2014

Hit & Miss Sample Mean Model The Hit & Miss Sample Mean Model investigates Monte Carlo integration techniques. The user is able to input a 1D integrand and finite integration limits and specify the number of trials and the number of separate runs. The simulation allows the user to study the …
The Hit & Miss Sample Mean Model investigates Monte Carlo integration techniques. The user is able to input a 1D integrand and finite integration limits and specify the number of trials and the number of separate runs. The simulation allows the user to study the dependence of the uncertainty/error on the number of trials.

Monte Carlo Integration wiht Importance Sampling Model The Monte Carlo Integration wiht Importance Sampling Model implements the sample mean and importance sampling techniques of Monte Carlo integration. The user can input a 1D integrand and finite integration limits and specify the required Monte Carlo technique or …
The Monte Carlo Integration wiht Importance Sampling Model implements the sample mean and importance sampling techniques of Monte Carlo integration. The user can input a 1D integrand and finite integration limits and specify the required Monte Carlo technique or techniques.

The importance sampling technique requires the user to input a normalized probability distribution p(x) that matches the integrand in its main features, and an associated transformation law x(y) that converts a uniform y in [0,1) into an x sampled according to p(x). The simulation does perform a check on the normalization of p(x), but the result may be inaccurate for distributions defined over a very large interval or those with strange behavior. The number of trials and the number of separate runs can be chosen by the user.

Hit & Miss Sample Mean Source Code
The source code zip archive contains an XML representation of the Hit & Miss Sample Mean Model. Unzip this archive in your EjsS 5 workspace to compile and run this model using EjsS 5 or above. download 12kb .zip
Last Modified: March 8, 2014

Monte Carlo Integration wiht Importance Sampling Source Code
The source code zip archive contains an XML representation of the Monte Carlo Integration wiht Importance Sampling Model. Unzip this archive in your EjsS 5 workspace to compile and run this model using EjsS 5 or above. download 10kb .zip
Last Modified: March 8, 2014

S. Wheaton, Computer Program COMPUTATIONAL PHYSICS RESOURCES: MONTE CARLO INTEGRATION & IMPORTANCE SAMPLING (2014), WWW Document, (http://www.compadre.org/Repository/document/ServeFile.cfm?ID=13200&DocID=3755).

S. Wheaton, Computer Program COMPUTATIONAL PHYSICS RESOURCES: MONTE CARLO INTEGRATION & IMPORTANCE SAMPLING (2014), <http://www.compadre.org/Repository/document/ServeFile.cfm?ID=13200&DocID=3755>.

Wheaton, S. (2014). Computational Physics Resources: Monte Carlo Integration & Importance Sampling [Computer software]. Retrieved December 7, 2016, from http://www.compadre.org/Repository/document/ServeFile.cfm?ID=13200&DocID=3755

%0 Computer Program %A Wheaton, Spencer %D 2014 %T Computational Physics Resources: Monte Carlo Integration & Importance Sampling %U http://www.compadre.org/Repository/document/ServeFile.cfm?ID=13200&DocID=3755

Disclaimer: ComPADRE offers citation styles as a guide only. We cannot offer interpretations about citations as this is an automated procedure. Please refer to the style manuals in the Citation Source Information area for clarifications.

The Easy Java Simulations Modeling and Authoring Tool is needed to explore the computational model used in the Computational Physics Resources: Monte Carlo Integration & Importance Sampling.