Some of the material in is restricted to members of the community. By logging in, you may be able to gain additional access to certain collections or items. If you have questions about access or logging in, please use the form on the Contact Page.
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.
A Thesis Submitted to the Department of Physics in Partial FulﬁLlment of the Requirements for the Degree of Master of Science.
Includes bibliographical references.
Harrison B. Prosper, Professor Directing Thesis; Todd Adams, Committee Member; Vasken Hagopian, Committee Member.
Florida State University
Use and Reproduction
This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s). The copyright in theses and dissertations completed at Florida State University is held by the students who author them.