Bayesian optimization as a probabilistic meta-program
Name
967346485-MIT.pdf
Description
Full printable version
Size
3.57 MB
Format
Adobe PDF
Checksum (MD5)
336450a0100ad025b5874120bff4bc75
Author(s)
Zinberg, Ben (Ben I.)
Advisor(s)
Vikash K. Mansinghka.
Date Issued
2015
Publisher
Massachusetts Institute of Technology
Abstract
This thesis answers two questions: 1. How should probabilistic programming languages in- corporate Gaussian processes? and 2. Is it possible to write a probabilistic meta-program for Bayesian optimization, a probabilistic meta-algorithm that can combine regression frameworks such as Gaussian processes with a broad class of parameter estimation and optimization techniques? We answer both questions affirmatively, presenting both an implementation and informal semantics for Gaussian process models in probabilistic programming systems, and a probabilistic meta-program for Bayesian optimization. The meta-program exposes modularity common to a wide range of Bayesian optimization methods in a way that is not apparent from their usual treatment in statistics.
Description
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (page 50).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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