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Abstract
Uncertainty is ubiquitous in the real world, and probability provides a sound way to reason under uncertainty. This fact has led to a plethora of probabilistic representation languages such as Bayesian networks, hidden Markov models and stochastic context-free grammars. More recently, we have developed new probabilistic languages that reason at the level of object, such as object-oriented Bayesian networks and probabilistic relational models. The wide variety of languages leads to the question of whether a general purpose probabilistic modeling language can be developed that encompasses all of them. This talk will describe IBAL, an attempt at developing such a language. After presenting the IBAL language, motivating considerations for the inference algorithm will be discussed, and the mechanism for IBAL inference will be described.