Control Functionals

Implementation of the control functional method for computing Bayesian posterior integrals


Updated 15 Mar 2017

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This is a compact implementation of the "control functional" method for Monte Carlo integration proposed in Oates, Chopin and Girolami, Journal of the Royal Statistical Society, Series B, 2017. The basic idea is that gradient information on the posterior distribution in Bayesian statistics can be used to better estimate integrals with respect to the posterior, via Stein's method.

Cite As

Chris Oates (2023). Control Functionals (, MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R2016a
Compatible with any release
Platform Compatibility
Windows macOS Linux
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Version Published Release Notes

Fixed a typo in the pre-amble.
Now works in dimension d = 1,2,3 or 4.

Improved the example usage in the pre-amble so that it generalises to d = 2,3.
Fixed the numerical regulariser to match the method given in the paper. Thanks to Min Hyung Kang!

In version 1.0 the likelihood function was incorrect. The wrong covariance matrix was used. This is now fixed.

Renamed zip folder
Included description of the main function.