two objects of class <code>bn</code>, corresponding to the numerator
 and the denominator models in the Bayes factor.

num, den

a data frame containing the data to be used to compute the Bayes
 factor.

data

a character string, the label of a posterior network score.
 If none is specified, the default score is the Bayesian Dirichlet
 equivalent score (<code>bde</code>) for discrete networks and the Bayesian
 Gaussian score (<code>bge</code>) for Gaussian networks. Other kinds of Bayesian
 networks are not currently supported.

score

extra tuning arguments for the posterior scores. See
 <code><a rd-options="" href="/link/score?package=bnlearn&version=4.7.1" data-mini-rdoc="bnlearn::score">score</a></code> for details.

&#8230;

a boolean value. If <code>TRUE</code> the Bayes factor is given as
 log(BF).

Compute the Bayes factor between the structures of two Bayesian networks..

network scores

structure learning

Bayesian network structure learning, parameter learning and inference.
This package implements constraint-based (PC, GS, IAMB, Inter-IAMB, Fast-IAMB, MMPC,
Hiton-PC, HPC), pairwise (ARACNE and Chow-Liu), score-based (Hill-Climbing and Tabu
Search) and hybrid (MMHC, RSMAX2, H2PC) 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, cross-validation, bootstrap and model averaging.
Development snapshots with the latest bugfixes are available from <https://www.bnlearn.com/>.

Marco Scutari

bnlearn

Bayesian Network Structure Learning, Parameter Learning and
Inference

BF function

<dl> <dt>num, den</dt>
<dd>two objects of class <code>bn</code>, corresponding to the numerator
 and the denominator models in the Bayes factor.</dd> <dt>data</dt>
<dd>a data frame containing the data to be used to compute the Bayes
 factor.</dd> <dt>score</dt>
<dd>a character string, the label of a posterior network score.
 If none is specified, the default score is the Bayesian Dirichlet
 equivalent score (<code>bde</code>) for discrete networks and the Bayesian
 Gaussian score (<code>bge</code>) for Gaussian networks. Other kinds of Bayesian
 networks are not currently supported.</dd> <dt>...</dt>
<dd>extra tuning arguments for the posterior scores. See
 <code>score</code> for details.</dd> <dt>log</dt>
<dd>a boolean value. If <code>TRUE</code> the Bayes factor is given as
 log(BF).</dd></dl>

Arguments

Author

Compute the Bayes factor between the structures of two Bayesian networks..

Bayes factor between two network structures — BF

<dl>

 <dt>num, den</dt>
<dd>two objects of class <code>bn</code>, corresponding to the numerator
 and the denominator models in the Bayes factor.</dd>

 <dt>data</dt>
<dd>a data frame containing the data to be used to compute the Bayes
 factor.</dd>

 <dt>score</dt>
<dd>a character string, the label of a posterior network score.
 If none is specified, the default score is the Bayesian Dirichlet
 equivalent score (<code>bde</code>) for discrete networks and the Bayesian
 Gaussian score (<code>bge</code>) for Gaussian networks. Other kinds of Bayesian
 networks are not currently supported.</dd>

 <dt>...</dt>
<dd>extra tuning arguments for the posterior scores. See
 <code>score</code> for details.</dd>

 <dt>log</dt>
<dd>a boolean value. If <code>TRUE</code> the Bayes factor is given as
 log(BF).</dd>

</dl>

BF: Bayes factor between two network structures

Description

Usage

Value

Arguments

Author

See Also

Examples