Optimal Bayesian experimental design in the presence of model error
Name
911916203-MIT.pdf
Description
Full printable version
Size
10.27 MB
Format
Adobe PDF
Checksum (MD5)
9ce0d5d965707941eaaa81fde0e963fa
Author(s)
Feng, Chi, S.M. Massachusetts Institute of Technology
Advisor(s)
Youssef M. Marzouk.
Date Issued
2015
Publisher
Massachusetts Institute of Technology
Abstract
The optimal selection of experimental conditions is essential to maximizing the value of data for inference and prediction. We propose an information theoretic framework and algorithms for robust optimal experimental design with simulation-based models, with the goal of maximizing information gain in targeted subsets of model parameters, particularly in situations where experiments are costly. Our framework employs a Bayesian statistical setting, which naturally incorporates heterogeneous sources of information. An objective function reflects expected information gain from proposed experimental designs. Monte Carlo sampling is used to evaluate the expected information gain, and stochastic approximation algorithms make optimization feasible for computationally intensive and high-dimensional problems. A key aspect of our framework is the introduction of model calibration discrepancy terms that are used to "relax" the model so that proposed optimal experiments are more robust to model error or inadequacy. We illustrate the approach via several model problems and misspecification scenarios. In particular, we show how optimal designs are modified by allowing for model error, and we evaluate the performance of various designs by simulating "real-world" data from models not considered explicitly in the optimization objective.
Description
Thesis: S.M., Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2015.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 87-90).
Subjects
Computation for Design and Optimization Program.
MIT Department
Massachusetts Institute of Technology. Computation for Design and Optimization Program
Terms of Use
M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
Persistent DSpace Link
