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A Bayesian Search for the Needle in the Haystack

July 30, 2015

Editor’s note: This post is part of a series showcasing Barcelona GSE master projects by students in the Class of 2015. The project is a required component of every master program.


Author: 
Timothée Stumpf-Fétizon

Master’s Program:
Data Science

Paper Abstract:

I develop an extension to Monte Carlo methods that sample from large and complex model spaces. I assess the extension using a new and fully functional module for Bayesian model choice. In standard conditions, my extension leads to an increase of around 30 percent in sampling efficiency.

Presentation Slides:

This is work in progress and there is no telling whether the rule works better in all situations!

If you’re interested in using BMA in practice, you can fork the software on my github (working knowledge of Python required!)

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