First-order probabilistic inference

David Poole

in Proceedings IJCAI 2003., Acapulco, Mexico, August 2003, pages 985-991.


There have been many proposals for first-order belief networks (i.e., where we quantify over individuals) but these typically only let us reason about the individuals that we know about. There are many instances where we have to quantify over all of the individuals in a
population. When we do this the population size often matters and we need to reason about all of the members of the population (but not necessarily individually). This paper presents an algorithm to reason about multiple individuals, where we may know particular facts about some of them, but want to treat the others as a group. Combining unification with variable elimination lets us reason about classes of individuals without needing to ground out the theory.

A revised version of the paper is available in pdf format. This corrects some mixing of the words, "variable" and "paramter", fixes Figure 3 and example 7.

You can also get the slides from the talk.