alpha.star

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>).

a data frame containing the variables in the model.

data

a boolean value. If <code>TRUE</code> a lot of debugging output is
 printed; otherwise the function is completely silent.

debug

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

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

alpha.star function

<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>

Arguments

Author

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

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

Value

Arguments

Author

References

Examples