## 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. |