In Bayesian analysis, means are commonly used to summarize Bayesian posterior distributions. Problems with a large number of parameters often require numerical integrations over many dimensions to obtain means. In this dissertation, posterior modes with respect to appropriate measures are used to summarize Bayesian posterior distributions, using the Newton-Raphson method to locate modes. Further inference of modes relies on the normal approximation, using asymptotic multivariate normal distributions to approximate posterior distributions. These techniques are applied to two statistical estimation problems. First, Bayesian sequential dose selection procedures are developed for Bioassay problems using Ramsey's prior [28]. Two adaptive designs for Bayesian sequential dose selection and estimation of the potency curve are given. The relative efficiency is used to compare the adaptive methods with other non-Bayesian methods (Spearman-Karber, up-and-down, and Robbins-Monro) for estimating the ED50 . Second, posterior distributions of the order of an autoregressive (AR) model are determined following Robb's method (1980). Wolfer's sunspot data is used as an example to compare the estimating results with FPE, AIC, BIC, and CIC methods. Both Robb's method and the normal approximation for estimation of the order have full posterior results. / Graduation date: 1992

University of Macau / Faculty of Science and Technology / Department of Mathematics

In order to facilitate software interaction and increase user satisfaction, various research efforts have tackled the problem of software customization by modeling the user’s goals, skills, and preferences. In this thesis, we focus on run-time solutions for adapting various interface and interaction aspects of software. From an intelligent agent’s perspective, the system views this customization problem as a decision-theoretic planning problem under uncertainty about the user. We propose a methodological framework for developing intelligent software interaction and assistance. This framework has been instantiated in various case studies which are reviewed in the thesis. Through efforts of data collection experiments to learn model parameters, simulation experiments to assess system feasibility and adaptivity, and usability testing to assess user receptiveness, our case studies show that our approach can effectively carry out customizations according to different user preferences and adapt to changing preferences over time.

In order to facilitate software interaction and increase user satisfaction, various research efforts have tackled the problem of software customization by modeling the user’s goals, skills, and preferences. In this thesis, we focus on run-time solutions for adapting various interface and interaction aspects of software. From an intelligent agent’s perspective, the system views this customization problem as a decision-theoretic planning problem under uncertainty about the user. We propose a methodological framework for developing intelligent software interaction and assistance. This framework has been instantiated in various case studies which are reviewed in the thesis. Through efforts of data collection experiments to learn model parameters, simulation experiments to assess system feasibility and adaptivity, and usability testing to assess user receptiveness, our case studies show that our approach can effectively carry out customizations according to different user preferences and adapt to changing preferences over time.

I combine theory, experiments and econometrics to undertake the task of disentangling the subtleties and implications of the distinction between risk, uncertainty and ambiguity. One general conclusion is that the elements of this methodological trilogy are not equally advanced. For example, new experimental tools must be developed to adequately test the predictions of theory. My dissertation is an example of this dynamic between theoretical and applied economics.

No description available.

Thesis (Ph.D.)--Utrecht University, 2006. / Includes bibliographical references (p. [133]-137).

Thesis (Ph. D.)--University of Hong Kong, 2001. / Includes bibliographical references (leaves 192-202).

Thesis (Ph. D.)--University of Missouri-Columbia, 2001. / Typescript. Vita. Includes bibliographical references (leaves 132-137). Also available on the Internet.

Thesis (Ph. D.)--Rutgers University, 2008. / "Graduate Program in Statistics and Biostatistics." Includes bibliographical references (p. 53-55).