. The simplest one is based on finite types and no subtyping, and the most powerful one has both recursive types and subtyping. Open until now is the question of type inference, and in the presence of subtyping "the absence of minimum typings poses practical problems for type inference" [2

Abstract. This EM review article focuses on parameter expansion, a simple technique introduced in the PX-EM algorithm to make EM converge faster while maintaining its simplicity and stability. The primary objective concerns the connection between parameter expansion and efficient inference

Abstract. This EM review article focuses on parameter expansion, a simple technique introduced in the PX-EM algorithm to make EM converge faster while maintaining its simplicity and stability. The primary objective concerns the connection between parameter expansion and efficient inference

Partial types for the -calculus were introduced by Thatte in 1988 [8] as a means of typing objects that are not typable with simple types, such as heterogeneous lists and persistent data. In that paper he showed that type inference for partial types was semidecidable. Decidability remained open

Permutations are ubiquitous in many real world problems, such as voting, rankings and data association. Representing uncertainty over permutations is challenging, since there are n! possibilities, and typical compact representations such as graphical models cannot efficiently capture the mutual

the problem from the combinatorial optimization point of view and state that efficient probabilistic inference in a belief network is a problem of finding an optimal factoring given a set of probability distributions. From this viewpoint, previously developed algorithms can be seen as alternate factoring

proportional to the Zength of the short-est derivation of the query and is independent of the size of the knowledge base. This work suggests that the expressiveness and the infer-ential ability of a representation and reasoning systems may be limited in unusual ways to ar-rive at extremely efficient yet fairly

, and linearizability. Our techniques enable successful verification of programs that were not be handled by previous concurrent shape analysis algorithms. We present our techniques in an abstract framework we call thread-correlation analysis. Thread-correlation analysis infers invariants that capture the correlations

mixtures of normal distributions. Efficient simulation methods are used to approximate various prior, posterior, and predictive distributions. This allows for direct inference on a variety of practical issues, including problems of local versus global smoothing, uncertainty about density estimates

The goal of my research is to uncover fundamental principles for the construction of real-time AI systems that employ declarative knowledge representations and general reasoning engines. There are many advantages to such an architecture: The same knowledge can be used for multiple tasks, such as diagnosis, prediction, control, and explanation. General knowledge can be applied to novel problems and environments. New, improved reasoning engines can be used without re-engineering the entire system.