Public Member Functions | |
SampleSATPriors (PossibleWorld state, WorldVariables vars, Iterable<?extends AbstractVariable > db, BeliefNetworkEx bn) throws Exception | |
Protected Member Functions | |
void | setRandomState () |
Package Attributes | |
BeliefNetworkEx | bn |
Random | generator |
HashMap< BeliefNode, double[]> | priors = null |
SampleSAT samples uniformly from the set of solutions. If there is a large number of solutions and many of them are improbable, then the sampled distribution may be dominated by a few high-probability worlds that were sampled. To prevent this, we use the prior to initialize random variables, thus introducing a slight bias towards worlds with higher probability.
Definition at line 93 of file SATIS.java.
edu::tum::cs::srl::bayesnets::inference::SATIS::SampleSATPriors::SampleSATPriors | ( | PossibleWorld | state, | |
WorldVariables | vars, | |||
Iterable<?extends AbstractVariable > | db, | |||
BeliefNetworkEx | bn | |||
) | throws Exception [inline] |
Definition at line 99 of file SATIS.java.
void edu::tum::cs::srl::bayesnets::inference::SATIS::SampleSATPriors::setRandomState | ( | ) | [inline, protected] |
sets a random state for non-evidence atoms
Reimplemented from edu::tum::cs::logic::sat::SampleSAT.
Definition at line 105 of file SATIS.java.
Definition at line 95 of file SATIS.java.
Definition at line 97 of file SATIS.java.
HashMap<BeliefNode, double[]> edu::tum::cs::srl::bayesnets::inference::SATIS::SampleSATPriors::priors = null [package] |
Definition at line 96 of file SATIS.java.