Bayesian networks provide a powerful intelligent information fusion architecture for modeling probabilistic and causal patterns involving multiple random variables. This paper advances a computable theory of learning discrete Bayesian networks from data. The theory is based on the MAP-MDL principles for maximizing the joint probability or interchangeably miniziming the joint description length ...

Due to their many applications, large Bayesian games have been a subject of growing interest in game theory and related fields. But to a large extent, models (1) have been restricted to one-shot interaction, (2) are based on an assumption that player types are independent, and (3) assume that the number of players is known. The current paper develops a general theory of repeated large Bayesian ...

In record linkage, or exact file matching, one compares two or more files on a single population for purposes of unduplication or production of an enhanced, merged database. Record linkage has many applications, including in population enumeration efforts, to create databases for epidemiological investigations, and to improve survey sample frames. Latent class and mixture models have been used ...

Proponents of Bayesian confirmation theory believe that they have the solution to a significant, recalcitrant problem in philosophy of science. It is the identification of the logic that governs evidence and its inductive bearing in science. That is the logic that lets us say that our catalog of planetary observations strongly confirms Copernicus’ heliocentric hypothesis; or that the fossil rec...

We argue that Bayesian decision theory provides a good theoretical framework for visual perception. Such a theory involves a likelihood function specifying how the scene generates the image(s), a prior assumption about the scene, and a decision rule to determine the scene interpretation. This is illustrated by describing Bayesian theories for individual visual cues and showing that perceptual b...

I. Bayesian Nash Equilibria of Games of Incomplete Information A. Very often it is natural to assume that the \physically" possible strategies are commonly known, but that the agents have private information concerning the payo s. 1. In principle it is not only each agent's information about payo s that is relevant, but also the agents' information about each others information about payo s, th...

Bayesian approaches to reasoning under uncertainty have gained substantially in popularity recently, due to the efforts of AI researchers and statisticians to find tractable means of probability propagation in restricted domains, namely those properly described by causal (Bayesian) networks (cf. Pearl 1988 and Neapolitan 1990). Most Bayesians are satisfied with such an approach to automating Ba...

Theory refinement is the task of updating a domain theory in the light of new cases, to be done automatically or with some expert as­ sistance. The problem of theory refinement under uncertainty is reviewed here in the con­ text of Bayesian statistics, a theory of belief revision. The problem is reduced to an incre­ mental learning task as follows: the learning system is initially primed with a...

Theory reenement is the task of updating a domain theory in the light of new cases, to be done automatically or with some expert assistance. The problem of theory reenement under uncertainty is reviewed here in the context of Bayesian statistics, a theory of belief revision. The problem is reduced to an incre-mental learning task as follows: the learning system is initially primed with a partia...