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 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. 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.