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Introduction

Learning a Bayesian network can be split into two problems which are both implemented in this package:

• Structure learning: Given a set of data samples, estimate a DAG that captures the dependencies between the variables.

• Parameter learning: Given a set of data samples and a DAG that captures the dependencies between the variables, estimate the (conditional) probability distributions of the individual variables.

The library supports Parameter learning for discrete nodes:

• Maximum Likelihood Estimation

• Bayesian Estimation

Structure learning for discrete, fully observed networks:

• Score-based structure estimation (BIC/BDeu/K2 score; exhaustive search, hill climb/tabu search)

• Constraint-based structure estimation (PC)

• Hybrid structure estimation (MMHC)

The following functions are available after importing bnlearn:

• Structure learning

• Parameter learning

• Inference

• Sampling

• Plot

• comparing two networks

• loading bif files

• conversion of directed to undirected graphs