The title of my thesis is
The Minimum Description Length Principle and Reasoning under Uncertainty
ILLC Dissertation Series DS 1998-03
The thesis consists of three parts, which can be read (more or less) separately. Part I contains a basic introduction to the MDL Principle and to its central concept, the Stochastic Complexity. It can be read without knowledge of information theory. It also contains some new theoretical developments concerning MDL that focus on the following question:
under what circumstances is it safe or even advisable to use overly simplistic models for the data at hand?
Part II contains some applications of MDL and Bayesian Methods. Part III concerns work I have done in a somewhat different field: Nonmonotonic Temporal Reasoning, specifically Reasoning about Action and the Frame Problem.