Thesis (Ph. D.)--University of Chicago, Dept. of Economics, June 2002. / Includes bibliographical references. Also available on the Internet.

Project report (M.S.) -- Worcester Polytechnic Institute. / Keywords: Bayesian; NFL; Bradley-Terry. Includes bibliographical references (p. 54-55).

Thesis--University of Wisconsin--Madison. / Typescript. Vita. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 64-66).

Thesis (Ph. D.)--University of Wisconsin--Madison, 1982. / Typescript. Vita. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 156-159).

Thesis (M.S.)--University of Missouri-Columbia, 2005. / The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file viewed on (July 13, 2006) Includes bibliographical references.

Thesis (M.S.) -- Worcester Polytechnic Institute. / Keywords: Gibbs Sampler; Bayesian Statistical Analysis; Ohlson Model; GIC Includes bibliographical references (p.79-80).

Thesis (Ph. D.)--Florida State University, 2006. / Advisor: Amy Chan Hilton, Florida State University, College of Engineering, Dept. of Civil and Environmental Engineering. Title and description from dissertation home page (viewed Sept. 18, 2006). Document formatted into pages; contains xiii, 107 pages. Includes bibliographical references.

Thesis (Ph.D.)--Baylor University, 2006. / Includes bibliographical references (p. 77-81).

Thesis (Ph.D.)--Baylor University, 2006. / Includes bibliographical references (p. 115-117).

In many scientific and engineering researches, Bayesian surrogate models are utilized to handle nonlinear data for regression and classification tasks. In this thesis, we consider a real-life problem, functional response modeling of metamaterial and its rapid design, to which we establish and test such models. To familiarize with this subject, some fundamental electromagnetic physics are provided.. Noticing that the dispersive data are usually in rational form, a two-stage modeling approach is proposed, where in the first stage, a universal link function is formulated to rationally approximate the data with a few discrete parameters, namely poles and residues. Then they are used to synthesize equivalent circuits, and surrogate models are applied to circuit elements in the second stage.. To start with a regression scheme, the classical Gaussian process (GP) is introduced, which proceeds by parameterizing a covariance function of any continuous inputs, and infers hyperparameters given the training data. Two metamaterial prototypes are illustrated to demonstrate the methodology of model building, whose results are shown to prove the efficiency and precision of probabilistic pre- dictions. One well-known problem with metamaterial functionality is its great variability in resonance identities, which shows discrepancy in approximation orders required to fit the data with rational functions. In order to give accurate prediction, both approximation order and the presenting circuit elements should be inferred, by classification and regression, respectively. An augmented Bayesian surrogate model, which integrates GP multiclass classification, Bayesian treed GP regression, is formulated to provide a systematic dealing to such unique physical phenomenon. Meanwhile, the nonstationarity and computational complexity are well scaled with such model.. Finally, as one of the most advantageous property of Bayesian perspective, probabilistic assessment to underlying uncertainties is also discussed and demonstrated with detailed formulation and examples.