package marginalmap
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trait
AnytimeMarginalMAP
extends MarginalMAPAlgorithm with Anytime
Anytime algorithms that compute most likely values of some elements, and marginalize over all other elements.
Anytime algorithms that compute most likely values of some elements, and marginalize over all other elements. A class that implements this trait must implement initialize, runStep, and computeMostLikelyValue methods.
 class AnytimeProbEvidenceMarginalMAP extends ProbEvidenceMarginalMAP with AnytimeSampler with AnytimeMarginalMAP
 trait AnytimeStructuredMarginalMAP extends StructuredMarginalMAPAlgorithm with AnytimeStructured with AnytimeMarginalMAP
 trait DecompositionMarginalMAP extends StructuredMarginalMAPAlgorithm with OneTimeStructuredMarginalMAP with DecompositionStructuredAlgorithm

trait
MarginalMAPAlgorithm
extends Algorithm
Algorithms that compute max a posteriori (MAP) values of some elements, and marginalize over all other elements.
 abstract class MarginalMAPBeliefPropagation extends MarginalMAPAlgorithm with ProbabilisticBeliefPropagation

class
MarginalMAPVEStrategy
extends RaisingStrategy
A solving strategy that uses MPE VE to solve nonnested problems, and performs the MAP step at the top level.
A solving strategy that uses MPE VE to solve nonnested problems, and performs the MAP step at the top level. It is assumed that at the top level, "toPreserve" variables are the MAP variables.

trait
OneTimeMarginalMAP
extends MarginalMAPAlgorithm with OneTime
Onetime algorithms that compute the most likely values of some elements, and marginalize over others.
Onetime algorithms that compute the most likely values of some elements, and marginalize over others. A class that implements this trait must implement run and computeMostLikelyValue methods.
 class OneTimeProbEvidenceMarginalMAP extends ProbEvidenceMarginalMAP with OneTimeSampler with OneTimeMarginalMAP
 trait OneTimeStructuredMarginalMAP extends StructuredMarginalMAPAlgorithm with OneTimeStructured with OneTimeMarginalMAP

abstract
class
ProbEvidenceMarginalMAP
extends MetropolisHastings with MarginalMAPAlgorithm
An algorithm for marginal MAP.
An algorithm for marginal MAP. This algorithm works by searching for the assignment to the MAP elements that maximizes the probability of evidence of observing that assignment. Uses one time probability of evidence sampling at each iteration for the given number of samples. Since the probability of evidence is just an estimate, this algorithm is allowed to repeatedly take more probability of evidence samples until it believes with high confidence that one state is better than another state, or it has reached the maximum number of allowed runs. The maximization is done by simulated annealing.

abstract
class
StructuredMarginalMAPAlgorithm
extends StructuredAlgorithm with MarginalMAPAlgorithm
A structured marginal MAP algorithm.

class
StructuredMarginalMAPVE
extends StructuredMarginalMAPAlgorithm with DecompositionMarginalMAP
A structured marginal MAP algorithm that uses VE to compute MAP queries.
Value Members
 object MarginalMAPBeliefPropagation
 object ProbEvidenceMarginalMAP
 object StructuredMarginalMAPVE