nak.inference.bp

BeliefPropagation

object BeliefPropagation

Implements basic belief propagation for computing variable marginals in graphical models.

For more powerful stuff, you should probably use Factorie. This is--imho--easier to use for "simple" problems.

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  1. case class Beliefs(model: Model, beliefs: IndexedSeq[DenseVector[Double]], messages: IndexedSeq[IndexedSeq[DenseVector[Double]]], factorLogPartitions: IndexedSeq[Double]) extends Product with Serializable

    The result object for BeliefPropagation, useful for getting information about marginals and edge marginals

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  13. def infer(model: Model, maxIterations: Int = 10, tolerance: Double = 1E-4): Beliefs

    Performs inference on the model, giving a Beliefs object with marginals

    Performs inference on the model, giving a Beliefs object with marginals

    model
    maxIterations
    tolerance
    returns

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