parallel integration

How to use the <pkg>bnlearn</pkg> package with the parallel computing
 environment provided by the <pkg>snow</pkg> or <pkg>parallel</pkg> packages.

documentation

Bayesian network structure learning, parameter learning and
  inference.
  This package implements constraint-based (GS, IAMB, Inter-IAMB, Fast-IAMB,
  MMPC, Hiton-PC), pairwise (ARACNE and Chow-Liu), score-based (Hill-Climbing
  and Tabu Search) and hybrid (MMHC and RSMAX2) structure learning algorithms
  for discrete, Gaussian and conditional Gaussian networks, along with many
  score functions and conditional independence tests.
  The Naive Bayes and the Tree-Augmented Naive Bayes (TAN) classifiers are
  also implemented.
  Some utility functions (model comparison and manipulation, random data
  generation, arc orientation testing, simple and advanced plots) are
  included, as well as support for parameter estimation (maximum likelihood
  and Bayesian) and inference, conditional probability queries and
  cross-validation. Development snapshots with the latest bugfixes are
  available from www.bnlearn.com.

Marco Scutari

bnlearn

Bayesian Network Structure Learning, Parameter Learning and
        Inference

parallel integration function

# load parallel and bnlearn and rsprng.
> library(parallel)
> library(bnlearn)
> cl = makeCluster(2)
# check it works.
> clusterEvalQ(cl, runif(10))
[[1]]
 [1] 0.9245585 0.1876445 0.3371175 0.2267916 0.0392876 0.9085125
 [7] 0.9041345 0.7408525 0.1537343 0.9503611[[2]]
 [1] 0.1932651 0.8218854 0.6087155 0.9037118 0.5257906 0.8737284
 [7] 0.5225114 0.8149691 0.1671706 0.6883363
# load the data.
> data(learning.test)
# call a learning function passing the cluster object (the
# return value of the previous makeCluster() call) as a
# parameter.
> res = gs(learning.test, cluster = cl)
# note that the number of test is evenly divided between
# the two nodes of the cluster.
> clusterEvalQ(cl, test.counter())
[[1]]
[1] 47[[2]]
[1] 42
# a few tests are still executed by this process.
> (test.counter())
[1] 4
# stop the cluster.
> stopCluster(cl)

parallel integration: bnlearn - snow/parallel package integration

Description

Arguments

Parallel computing for constraint-based algorithms