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Bayesian Neural Networks in Data-Intensive High Energy Physics Applications

This dissertation studies a graphical processing unit (GPU) construction of Bayesian neural networks (BNNs) using large training data sets. The goal is to create a program for the mapping of phenomenological Minimal Supersymmetric Standard Model (pMSSM) parameters to their predictions. This would allow for a more robust method of studying the Minimal Supersymmetric Standard Model, which is of much interest at the Large Hadron Collider (LHC) experiment CERN. A systematic study of the speedup achieved in the GPU application compared to a Central Processing Unit (CPU) implementation are presented. / A Dissertation submitted to the Department of Scientific Computing in partial fulfillment of the requirements for the degree of Doctor of Philosophy. / Spring Semester, 2014. / April 1, 2014. / Bayesian Neural Networks, GPU, pMSSM, Scientific Computing / Includes bibliographical references. / Anke Meyer-Baese, Professor Directing Dissertation; Harrison Prosper, Professor Directing Dissertation; Jorge Piekarewicz, University Representative; Sachin Shanbhag, Committee Member; Peter Beerli, Committee Member.

Identiferoai:union.ndltd.org:fsu.edu/oai:fsu.digital.flvc.org:fsu_185301
ContributorsPerry, Michelle (authoraut), Meyer-Baese, Anke (professor directing dissertation), Prosper, Harrison (professor directing dissertation), Piekarewicz, Jorge (university representative), Shanbhag, Sachin (committee member), Beerli, Peter (committee member), Department of Scientific Computing (degree granting department), Florida State University (degree granting institution)
PublisherFlorida State University, Florida State University
Source SetsFlorida State University
LanguageEnglish, English
Detected LanguageEnglish
TypeText, text
Format1 online resource, computer, application/pdf
RightsThis 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.

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