Graphical models are a marriage between probability theory and graph theory. They provide a natural tool for dealing with two problems that occur throughout applied mathematics and engineering... - Read more

The Association for Uncertainty in Artificial Intelligence (AUAI) is a non-profit organization focused on organizing the annual Conference on Uncertainty in Artificial Intelligence (UAI) and, more generally, on promoting research in pursuit of advances in knowledge representation, learning, and reasoning under uncertainty. - Read more

Our intention is to construct a repository that will allow us empirical research within our community by facilitating better reproducibility of results and better comparisons among competing approach. Both of these are required to measure progress on problems that are commonly agreed upon, such as inference and learning. - Read more

B-Course is a web-based data analysis tool for Bayesian modeling, in particular dependence and classification modeling. It can also be used as an interactive tutorial which provides you with data sets that have been prepared in advance. - Read more

We all know that beliefs can sometimes be wrong, so intelligent agents need to be able to revise beliefs when they acquire new information that contradicts their old beliefs. - Read more

Our main research focus is on dealing with complex domains that involve large amounts of uncertainty. Our work builds on the framework of probability theory, decision theory, and game theory, but uses techniques from artificial intelligence and computer science to allow us to apply this framework to complex real-world problems. - Read more

Bayesian Networks are becoming an increasingly important area for research and application in the entire field of Artificial Intelligence. This paper explores the nature and implications for Bayesian Networks beginning with an overview and comparison of inferential statistics and Bayes' Theorem. - Read more

Application of Bayesian belief networks in systems that interact directly with human users, such as decision support systems, requires effective user interfaces. The principal task of such interfaces is bridging the gap between probabilistic models and human intuitive approaches to modeling uncertainty. - Read more

Describes a new paradigm for implementing inference in belief networks, which consists of two steps: (1) compiling a belief network into an arithmetic expression called a Query DAG (Q-DAG); and (2) answering queries using a simple evaluation algorithm. - Read more