CalculateEdgeProbabilities: Calculate pairwise edge probabilities

In cia: Learn and Apply Directed Acyclic Graphs for Causal Inference

Calculate pairwise edge probabilities

Description

Calculate pairwise edge probabilities. The posterior probability of an edge E given the data D is given by marginalising out the graph structure g over the graph space G, such that

p(E|D) = \sum_{g \in G} p(E|g)p(g|D).

Usage

CalculateEdgeProbabilities(x, ...)

Arguments

x	A cia_chain(s) or collection object where states are DAGs.
...	Extra parameters sent to the methods. For a dag collection you can choose to use estimated p(g|D) in two ways which can be specified using the 'method' parameter.method='sampled' for MCMC sampled frequency (which is our recommended method) or method='score' which uses the normalised scores.

Details

The posterior probability for a given graph p(g|D) is estimated in two ways which can be specified using the 'method' parameter.

Value

Matrix of edge probabilities.

Examples

data <- bnlearn::learning.test

dag <- UniformlySampleDAG(colnames(data))
partitioned_nodes <- DAGtoPartition(dag)

scorer <- CreateScorer(
  scorer = BNLearnScorer, 
  data = data
  )

results <- SampleChains(10, partitioned_nodes, PartitionMCMC(), scorer)
dag_chains <- PartitiontoDAG(results, scorer)
CalculateEdgeProbabilities(dag_chains)

cia documentation built on April 4, 2025, 5:23 a.m.