Package edu.cmu.tetrad.search.score

Class PoissonPriorScore

java.lang.Object
edu.cmu.tetrad.search.score.PoissonPriorScore
All Implemented Interfaces:
Score, EffectiveSampleSizeSettable
public class PoissonPriorScore extends Object implements Score, EffectiveSampleSizeSettable
Implements Poisson prior score, a novel (unpubished) score that replaces the penalty term in BIC by the log of the Poisson distribution. The Poisson distribution has a lambda parameter, which is made a parameter of this score and acts like a structure prior for the score.

Here is the Wikipedia page for the Poisson distribution, for reference:

...

As for all scores in Tetrad, higher scores mean more dependence, and negative scores indicate independence.

Version:
$Id: $Id
Author:
bryanandrews, josephramsey

  • Constructor Details

    • PoissonPriorScore

      public PoissonPriorScore(ICovarianceMatrix covariances)
      Constructs the score using a covariance matrix.
      Parameters:
      covariances - The covariance matrix.

    • PoissonPriorScore

      public PoissonPriorScore(DataSet dataSet, boolean precomputeCovariances)
      Constructs the score using a covariance matrix.
      Parameters:
      dataSet - The dataset.
      precomputeCovariances - Whether the covariances should be precomputed or computed on the fly. True if

  • Method Details

    • localScoreDiff

      public double localScoreDiff(int x, int y, int[] z)
      Returns the score difference of the graph.

      Returns the score difference localScore(y | z, x) - localScore(y | z).

      Specified by:
      localScoreDiff in interface Score
      Parameters:
      x - A node.
      y - TAhe node.
      z - A set of nodes.
      Returns:
      The score difference.

    • localScore

      public double localScore(int i, int... parents) throws RuntimeException
      Returns the score of the node at index i, given its parents.
      Specified by:
      localScore in interface Score
      Parameters:
      i - The index of the node.
      parents - The indices of the node's parents.
      Returns:
      The score, or NaN if the score cannot be calculated.
      Throws:
      RuntimeException - if any.

    • getCovariances

      public ICovarianceMatrix getCovariances()
      Returns the covariance matrix.
      Returns:
      The covariance matrix.

    • getSampleSize

      public int getSampleSize()
      Returns the sample size.
      Specified by:
      getSampleSize in interface Score
      Returns:
      This size.

    • isEffectEdge

      public boolean isEffectEdge(double bump)
      Returns true iff the edge between x and y is an effect edge.

      Returns true if the edge with the given bump is an effect edge.

      Specified by:
      isEffectEdge in interface Score
      Parameters:
      bump - The bump.
      Returns:
      True iff the edge between x and y is an effect edge.

    • getVariables

      public List<Node> getVariables()
      The variables of the score.

      Returns the variables.

      Specified by:
      getVariables in interface Score
      Returns:
      This list.

    • getMaxDegree

      public int getMaxDegree()
      Returns the max degree, by default 1000.

      Returns the max degree.

      Specified by:
      getMaxDegree in interface Score
      Returns:
      The max degree.

    • determines

      public boolean determines(List<Node> z, Node y)
      Returns true iff the score determines the edge between x and y.

      Returns true if z determines y.

      Specified by:
      determines in interface Score
      Parameters:
      z - The set of nodes.
      y - The node.
      Returns:
      True iff the score determines the edge between x and y.

    • getData

      public DataModel getData()
      Returns the data set.
      Returns:
      The data set.

    • setLambda

      public void setLambda(double lambda)
      Sets the lambda parameter.
      Parameters:
      lambda - The lambda parameter.

    • setSingularityLambda

      public void setSingularityLambda(double singularityLambda)
      Sets the Singularity lambda value used in computations.
      Parameters:
      singularityLambda - The Singularity lambda parameter to set.

    • getEffectiveSampleSize

      public int getEffectiveSampleSize()
      Description copied from interface: EffectiveSampleSizeSettable
      Returns the effective sample size.
      Specified by:
      getEffectiveSampleSize in interface EffectiveSampleSizeSettable
      Returns:
      the effective sample size

    • setEffectiveSampleSize

      public void setEffectiveSampleSize(int nEff)
      Description copied from interface: EffectiveSampleSizeSettable
      Sets the effective sample size, or -1 if the actual sample size should be used.
      Specified by:
      setEffectiveSampleSize in interface EffectiveSampleSizeSettable
      Parameters:
      nEff - the effective sample size