# Inference Methods¶

## Full posterior distributions¶

The following algorithms compute the full posterior distribution over a set of variables given the evidence .

### Enumeration-Ask¶

Performs exact inference by enumerating all possible worlds that are consistent with the evidence , i.e.

Warning

This is intractable for all but the smallest reasoning problems.

### MC-SAT¶

Performs approximate inference using the MC-SAT algorithm.

### Gibbs Sampling¶

Performs Gibbs sampling on the ground MRF.

## Most Probable Explanation (MPE)¶

In some cases, one is not interested in the full posterior distribution over query variables given evidence , but only in the most probable variable assignment of pracmln provides two algorithms to perform this kind of MPE inference (which is sometimes also referred to as maximum a-posteriori (MAP) inference.

### MaxWalk-SAT¶

A randomized weighted satisfiability solver that performs simulated annealing.

Parameters:

`maxsteps`

: the maximum number simulated annealing steps`thr`

: the threshold for the sum of unsatisfied weighted formulas that needs be undercut for the algorithm to terminate`hardw`

: a constant weight that will temporarily be attached to hard logical formulas.

### WCSP¶

Performs exact MPE inference by converting the ground MRF into an equivalent weighted constraint satisfaction problem (WCSP) and solving it using the toulbar2 [AdGS10] solver. For more details, see [JMW09].

- AdGS10
D Allouche, S de Givry, and T Schiex. Toulbar2, an open source exact cost function network solver. Technical Report, Technical report, INRIA, 2010.

- JMW09
Dominik Jain, Paul Maier, and Gregor Wylezich. Markov Logic as a Modelling Language for Weighted Constraint Satisfaction Problems. In

*Eighth International Workshop on Constraint Modelling and Reformulation, in conjunction with CP2009*. 2009.