SampleChains: Sample chains

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

Sample chains

Description

Sample chains

Usage

SampleChains(
  n_results,
  init_state,
  transition,
  scorer,
  n_thin = 1,
  n_parallel_chains = 2
)

Arguments

n_results	Number of saved states per chain.
init_state	An initial state that can be passed to transition. This can be a single state or a list of states for each parallel chain.
transition	A transition function.
scorer	A scorer object.
n_thin	Number of steps between saved states.
n_parallel_chains	Number of chains to run in parallel. Default is 2.

Value

A cia_chains object.

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)

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