Type of Document	Thesis
Author	Saucedo, Skyler Richard
Author's Email Address	skylersaucedo@yahoo.com
URN	etd-11152006-184422
Title	Bayesian Neural Networks for Classification
Degree	Master of Science
Department	Physics, Department of
Advisory Committee	Advisor Name Title Harrison Prosper Committee Chair Todd Adams Committee Member Vasken Hagopian Committee Member
Keywords	LHC mSUGRA CMS Hybrid Markov Chain Monte Carlo Neural Networks Bayesian SUSY Autocorrelation D0
Date of Defense	2006-11-10
Availability	unrestricted
Abstract We study a model for classification based upon Bayesian statistics. The model, called Bayesian Neural Networks (BNN), is based on a function that is a weighted sum of hyperbolic tangent functions. To illustrate this method, we apply it to the task of separating Supersymmetric (SUSY) events from Standard Model proton-proton events at the LHC. Unlike conventional Neural Networks, the BNN model is an average over networks, which is done by integrating over a high dimensional parameter space. Since integrating over the parameter space is analytically impossible, the BNN method uses Hybrid Markov Chain Monte Carlo techniques to sample the desired probability densities while preserving a high acceptance rate. In this thesis we study the correlation properties of a sequence of Neural Networks. The results of this study are of great importance because validate the strategy currently used by the D0 collaboration, in the search for single top quarks at Fermilab.
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