object MarginalMAPBeliefPropagation

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

    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): MarginalMAPBeliefPropagation with AnytimeProbabilisticBeliefPropagation with AnytimeMarginalMAP

    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): MarginalMAPBeliefPropagation with AnytimeProbabilisticBeliefPropagation with AnytimeMarginalMAP

    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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