Get incremental edges

Get edges that do not incrementally improve the score over an empty DAG greater than a cutoff. In detail, this returns the edges where a graph with the edge \(E\) given by \(g_E\) such that Score(g_E) - Score(g_empty) < cutoff. Assuming that the scorer returns the log of the marginalised posterior, then the cutoff corresponds to the log of the Bayes Factor. The output can be used as a blacklist.

Usage

GetIncrementalScoringEdges(scorer, cutoff = 0)

Arguments

scorer

A scorer object.

cutoff

A score cutoff. The score cutoff is equal to the log of the Bayes Factor between the two models.

Value

A Boolean matrix of (parent, child) pairs for blacklisting.

Examples

data <- bnlearn::learning.test

scorer <- CreateScorer(
  scorer = BNLearnScorer, 
  data = data
  )
  
blacklist <- GetIncrementalScoringEdges(scorer, cutoff = -10.0)

blacklist_scorer <- CreateScorer(
  scorer = BNLearnScorer, 
  data = data,
  cache = TRUE
  )

# Randomly sample a starting DAG consistent with the blacklist. Then
# convert to a partition.
dag <- UniformlySampleDAG(colnames(data)) * !blacklist
partitioned_nodes <- DAGtoPartition(dag)

results <- SampleChains(10, partitioned_nodes, PartitionMCMC(), blacklist_scorer)