Estimate the optimal imaginary sample size for the BDe score, given a network
 structure and a data set, assuming a uniform prior.

alpha.star

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

alpha.star function

Estimate the optimal imaginary sample size for the BDe score, given a network
 structure and a data set, assuming a uniform prior.

Estimate the optimal imaginary sample size for BDe(u) — alpha.star

<dl>

 <dt>x</dt>
<dd>an object of class <code>bn</code> (for <code>bn.fit</code> and <code>custom.fit</code>)
 or an object of class <code>bn.fit</code> (for <code>bn.net</code>).</dd>

 <dt>data</dt>
<dd>a data frame containing the variables in the model.</dd>

 <dt>debug</dt>
<dd>a boolean value. If <code>TRUE</code>, a lot of debugging output is
 printed. Otherwise the function is completely silent.</dd>

</dl>

alpha.star: Estimate the optimal imaginary sample size for BDe(u)

Description

Usage

Arguments

Value

References

Examples