belief.propagation

belief.propagation,InferenceEngine-method

belief.propagation,InferenceEngine

Perform belief propagation for the network of an InferenceEngine, given a set of observations.
In the current version of <code>bnstruct</code>, belief propagation can be computed only over a junction tree.

Bayesian Network Structure Learning from Data with Missing Values.
The package implements the Silander-Myllymaki complete search,
the Max-Min Parents-and-Children, the Hill-Climbing, the
Max-Min Hill-climbing heuristic searches, and the Structural
Expectation-Maximization algorithm. Available scoring functions are
BDeu, AIC, BIC. The package also implements methods for generating and using
bootstrap samples, imputed data, inference.

Alberto Franzin

bnstruct

Bayesian Network Structure Learning from Data with Missing
Values

Francesco Sambo

belief.propagation function

<dl><dt>ie</dt>
<dd>an <code>InferenceEngine</code> object.</dd>
<dt>observations</dt>
<dd>list of observations, consisting in two vector, <code>observed.vars</code> for the observed variables,
and <code>observed.vals</code> for the values taken by the variables listed in <code>observed.vars</code>. If no observations
are provided, the <code>InferenceEngine</code> will use the ones it already contains.</dd>
<dt>return.potentials</dt>
<dd>if TRUE only the potentials are returned, instead of the default <code>BN</code>.</dd></dl>

Arguments

perform belief propagation. — belief.propagation

<dl>

<dt>ie</dt>
<dd>an <code>InferenceEngine</code> object.</dd>


<dt>observations</dt>
<dd>list of observations, consisting in two vector, <code>observed.vars</code> for the observed variables,
and <code>observed.vals</code> for the values taken by the variables listed in <code>observed.vars</code>. If no observations
are provided, the <code>InferenceEngine</code> will use the ones it already contains.</dd>


<dt>return.potentials</dt>
<dd>if TRUE only the potentials are returned, instead of the default <code>BN</code>.</dd>

</dl>

belief.propagation: perform belief propagation.

Description

Usage

Value

Arguments