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Bayesian Neural Networks for Classification

Title: Bayesian Neural Networks for Classification.
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Name(s): Saucedo, Skyler Richard, author
Prosper, Harrison B., professor directing thesis
Adams, Todd, committee member
Hagopian, Vasken, committee member
Department of Physics, degree granting department
Florida State University, degree granting institution
Type of Resource: text
Genre: Text
Issuance: monographic
Date Issued: 2007
Publisher: Florida State University
Place of Publication: Tallahassee, Florida
Physical Form: computer
online resource
Extent: 1 online resource
Language(s): English
Abstract/Description: 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.
Identifier: FSU_migr_etd-2069 (IID)
Submitted Note: A Thesis Submitted to the Department of Physics in Partial FulfiLlment of the Requirements for the Degree of Master of Science.
Degree Awarded: Fall Semester, 2007.
Date of Defense: November 10, 2006.
Keywords: LHC, mSUGRA, CMS, Hybrid Markov Chain Monte Carlo, Neural Networks, Bayesian, SUSY, Autocorrelation, D0
Bibliography Note: Includes bibliographical references.
Advisory Committee: Harrison B. Prosper, Professor Directing Thesis; Todd Adams, Committee Member; Vasken Hagopian, Committee Member.
Subject(s): Physics
Persistent Link to This Record: http://purl.flvc.org/fsu/fd/FSU_migr_etd-2069
Owner Institution: FSU

Choose the citation style.
Saucedo, S. R. (2007). Bayesian Neural Networks for Classification. Retrieved from http://purl.flvc.org/fsu/fd/FSU_migr_etd-2069