We are concerned with algorithms for solving reasoning problems over graphical models, which includes common tasks for belief, constraint, and mixed networks. In the following we present some of the resulting work; this list is bound to grow over time.

See our Repository for some example problem instances.

Likelihood algorithms

Exact P(e)

These algorithms compute the exact probability of a given set of evidence in a Bayesian network.

Approximate P(e)

These algorithms compute the approximate probability of a given set of evidence in a Bayesian network.

Approximate Belief Updating (or posterior marginals)

These algorithms compute approximate beliefs for each variable in a network, given a certain set of evidence.

Compilation of AND/OR Multi-Valued Decision Diagrams (AOMDDs)

This algorithm compiles a weighted CSP or Bayesian network into an AOMDD.

Optimization algorithms


These algorithms compute the most likely tuple in a Bayesian network given some evidence.


These algorithms compute an optimal assignment in a weighted constraint satisfaction problem given some evidence.


This algorithm computes an optimal solution to an integer programming problem.

General purpose algorithms

Computing Elimination Orderings

Tree Decomposition

This algorithm decomposes a given graphical model (Bayesian, Markov or constraint network) into a tree decomposition.

Older software

Disclaimer: All software should be regarded as in development and is made available on an "as-is" basis, without warranty of any kind. It has in no way been thoroughly tested and might not function as intended or expected.

Software (last edited 2013-10-29 07:35:48 by WilliamLam)