Gradient-based approaches to direct policy search in reinforcement learning have received much recent attention as a means to solve problems of partial observability and to avoid some of the problems associated with policy degradation in value-function methods. In this paper we introduce GPOMDP,...

Recently, a computationally-efficient method was presented for calibrating a wide-class of Markov processes from discrete-sampled abundance data. The method was illustrated with respect to one-dimensional processes and required the assumption of stationarity. Here we demonstrate that the...

In this dissertation we present mathematical models that help answer health policy questions relating to HIV and Hepatitis C (HCV), and analyze bias in Markov models of disease progression. We begin by developing a Markov decision process model that examines the timing of testing and treatment...

This paper studies airline customers' online search and purchase behaviors. Twofundamental aspects of online behavior are examined: (1) the link between searchbehavior and buying behavior and (2) the evolution of inter-temporal search andpurchase decisions of strategic buyers.In the first study,...

This dissertation consists of three essays that apply both economic theory and econometric methods to understand design and dynamics of institutions. In particular, it studies how institutions aggregate information and deal with uncertainty and attempts to derive implications for optimal...