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Perry, M. (2014). Bayesian Neural Networks in Data-Intensive High Energy Physics Applications. Retrieved from http://purl.flvc.org/fsu/fd/FSU_migr_etd-8867
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.
Bibliography Note
Includes bibliographical references.
Advisory Committee
Anke Meyer-Baese, Professor Directing Dissertation; Harrison Prosper, Professor Directing Dissertation; Jorge Piekarewicz, University Representative; Sachin Shanbhag, Committee Member; Peter Beerli, Committee Member.
Publisher
Florida State University
Identifier
FSU_migr_etd-8867
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Perry, M. (2014). Bayesian Neural Networks in Data-Intensive High Energy Physics Applications. Retrieved from http://purl.flvc.org/fsu/fd/FSU_migr_etd-8867
