Bayesian network structure learning, parameter learning and
inference
Description
Bayesian network structure learning (via constraint-based,
score-based and hybrid algorithms), parameter learning (via ML
and Bayesian estimators) and inference. This package
implements the Grow-Shrink (GS) algorithm, the Incremental
Association (IAMB) algorithm, the Interleaved-IAMB (Inter-IAMB)
algorithm, the Fast-IAMB (Fast-IAMB) algorithm, the Max-Min
Parents and Children (MMPC) algorithm, the Hiton-PC algorithm,
the ARACNE and Chow-Liu algorithms, the Hill-Climbing (HC)
greedy search algorithm, the Tabu Search (TABU) algorithm, the
Max-Min Hill-Climbing (MMHC) algorithm and the two-stage
Restricted Maximization (RSMAX2) algorithm for both discrete
and 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 and inference, conditional probability
queries and cross-validation.