|
| Compiling Bayesian Networks Using Variable Elimination |
| a conference paper |
| speaker: | Mark Chavira | | |
| occasion: | January 10, 2007, 0 h 20 m |
| location: | UCLA Boelter Hall, Los Angeles, California |
| abstract: | Compiling Bayesian networks has proven an effective approach for
inference that can utilize both global and local network structure.
In this paper, we define a new method of compiling based on variable
elimination (VE) and Algebraic Decision Diagrams (ADDs). The
approach is important for the following reasons. First, it exploits
local structure much more effectively than previous techniques based
on VE. Second, the approach allows any of the many VE
variants to compute answers to multiple queries simultaneously.
Third, the approach makes a large body of research into more
structured representations of factors relevant in many more
circumstances than it has been previously. Finally, experimental
results demonstrate that VE can exploit local structure as
effectively as state--of--the--art algorithms based on conditioning
on the networks considered, and can sometimes lead to much faster
compilation times.
|
| downloads: | |
|