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, MMPC,
Hiton-PC, HPC), pairwise (ARACNE and Chow-Liu), score-based (Hill-Climbing,
Tabu Search, DirectLiNGAM) and hybrid (MMHC, RSMAX2, H2PC) structure learning
algorithms for discrete, Gaussian, conditional Gaussian and zero-inflated
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, interventions,
counterfactuals, 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

Tomi Silander

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, a
 BIC scores or <code>"custom-score"</code> for the custom 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 currently
 supported by using their BIC score to approximate their marginal
 log-likelihoods.</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, a
 BIC scores or <code>"custom-score"</code> for the custom 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 currently
 supported by using their BIC score to approximate their marginal
 log-likelihoods.</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