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MarginalMAPBeliefPropagation 

Companion class MarginalMAPBeliefPropagation

object MarginalMAPBeliefPropagation

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4. def apply(dependentUniverses: List[(Universe, List[NamedEvidence[_]])], dependentAlgorithm: (Universe, List[NamedEvidence[_]]) ⇒ () ⇒ Double, targets: Element[_]*)(implicit universe: Universe)

Creates an Anytime marginal MAP belief propagation computer in the current default universe.

Creates an Anytime marginal MAP belief propagation computer in the current default universe.

dependentUniverses

Dependent universes for this algorithm.

dependentAlgorithm

Used to determine algorithm for computing probability of evidence in dependent universes.

targets

MAP elements, which can be queried. Elements not supplied here are summed over.

5. def apply(dependentUniverses: List[(Universe, List[NamedEvidence[_]])], dependentAlgorithm: (Universe, List[NamedEvidence[_]]) ⇒ () ⇒ Double, myIterations: Int, targets: Element[_]*)(implicit universe: Universe): MarginalMAPBeliefPropagation with OneTimeProbabilisticBeliefPropagation with OneTimeMarginalMAP { val iterations: Int }

Creates a One Time marginal MAP belief propagation computer in the current default universe.

Creates a One Time marginal MAP belief propagation computer in the current default universe.

dependentUniverses

Dependent universes for this algorithm.

dependentAlgorithm

Used to determine algorithm for computing probability of evidence in dependent universes.

myIterations

Iterations of mixed-product BP to run.

targets

MAP elements, which can be queried. Elements not supplied here are summed over.

6. def apply(dependentUniverses: List[(Universe, List[NamedEvidence[_]])], targets: Element[_]*)(implicit universe: Universe)

Creates an Anytime marginal MAP belief propagation computer in the current default universe.

Creates an Anytime marginal MAP belief propagation computer in the current default universe.

dependentUniverses

Dependent universes for this algorithm.

targets

MAP elements, which can be queried. Elements not supplied here are summed over.

7. def apply(dependentUniverses: List[(Universe, List[NamedEvidence[_]])], myIterations: Int, targets: Element[_]*)(implicit universe: Universe): MarginalMAPBeliefPropagation with OneTimeProbabilisticBeliefPropagation with OneTimeMarginalMAP { val iterations: Int }

Creates a One Time marginal MAP belief propagation computer in the current default universe.

Creates a One Time marginal MAP belief propagation computer in the current default universe.

dependentUniverses

Dependent universes for this algorithm.

myIterations

Iterations of mixed-product BP to run.

targets

MAP elements, which can be queried. Elements not supplied here are summed over.

8. def apply(targets: Element[_]*)(implicit universe: Universe)

Creates an Anytime marginal MAP belief propagation computer in the current default universe.

Creates an Anytime marginal MAP belief propagation computer in the current default universe.

targets

MAP elements, which can be queried. Elements not supplied here are summed over.

9. def apply(myIterations: Int, targets: Element[_]*)(implicit universe: Universe): MarginalMAPBeliefPropagation with OneTimeProbabilisticBeliefPropagation with OneTimeMarginalMAP { val iterations: Int }

Creates a One Time marginal MAP belief propagation computer in the current default universe.

Creates a One Time marginal MAP belief propagation computer in the current default universe.

myIterations

Iterations of mixed-product BP to run.

targets

MAP elements, which can be queried. Elements not supplied here are summed over.

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18. def mostLikelyValue[T](target: Element[T], mapElements: Element[_]*): T

Use belief propagation to compute the most likely value of the given element.

Use belief propagation to compute the most likely value of the given element. Runs 10 iterations of mixed-product BP.

target

Element for which to compute MAP value.

mapElements

Additional elements to MAP. Elements not in this list are summed over.

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