Defense Date

2022

Document Type

Dissertation

Degree Name

Doctor of Philosophy

Department

Systems Modeling and Analysis

First Advisor

Dr. David Edwards

Second Advisor

Dr. Edward Boone

Third Advisor

Dr. D'arcy Mays

Fourth Advisor

Dr. Paul Brooks

Abstract

Fully sequential optimal Bayesian experimentation can offer greater utility than both traditional Bayesian designs and greedy sequential methods, but practically cannot be solved due to numerical complexity and continuous outcome spaces. Approximate solutions can be found via approximate dynamic programming, but rely on surrogate models of the expected utility at each trial of the experiment with hand-chosen features or use methods which ignore the underlying geometry of the space of probability distributions. We propose the use of Gaussian process models indexed on the belief states visited in experimentation to provide utility-agnostic surrogate models for approximating Bayesian optimal sequential designs which require no feature engineering. This novel methodology for approximating Bayesian optimal sequential designs is then applied to conjugate models and to particle approximations for different batch sizes.

Rights

© The Author

Is Part Of

VCU University Archives

Is Part Of

VCU Theses and Dissertations

Date of Submission

8-12-2022

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