parallel integration

How to use the bnlearn package with the parallel computing environment
 provided by the snow or parallel 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 <http://www.bnlearn.com>.

Marco Scutari

bnlearn

Bayesian Network Structure Learning, Parameter Learning and
Inference

parallel integration function

<pre>
# load parallel and bnlearn and rsprng.
&gt; library(parallel)
&gt; library(bnlearn)
&gt; cl = makeCluster(2)
# check it works.
&gt; 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.
&gt; data(learning.test)
# call a learning function passing the cluster object (the
# return value of the previous makeCluster() call) as an
# argument.
&gt; res = gs(learning.test, cluster = cl)
# note that the number of test is evenly divided between
# the two nodes of the cluster.
&gt; clusterEvalQ(cl, test.counter())
[[1]]
[1] 47 [[2]]
[1] 42
# a few tests are still executed by this process.
&gt; (test.counter())
[1] 4
# stop the cluster.
&gt; stopCluster(cl)
</pre>

parallel integration: bnlearn - snow/parallel package integration

Description

Arguments

Parallel computing for constraint-based algorithms