Bayesian Network Repository

Several reference Bayesian networks are commonly used in literature as benchmarks. They are available in different formats from several sources, the most famous one being the Bayesian network repository hosted at the Hebrew University of Jerusalem. Others are shipped as examples of various Bayesian network-related software like Hugin or described in reference books such as Korb & Nicholson's “Bayesian Artificial Intelligence” or Koller & Friedman's “Probabilistic Graphical Models”.

Even more networks are available from various papers that used Bayesian networks to analyze data from various domains. This is especially true for Gaussian networks and conditional linear Gaussian networks, since the original Bayesian network repository included only discrete Bayesian networks.

Here I collected all the networks that I produced in my work, that I used for various simulations and that I used to testing the implementations of read.bif(), read.dsc() and read.net() in bnlearn. All discrete networks are available in the BIF, DSC and NET formats and have been quality-checked and fixed as needed (i.e. all conditional probability distributions sum to one, no dummy nodes with a single level, no dangling dependencies on non-existent nodes, etc.). R objects with the bn.fit objects for all networks are provided both as RDA and RDS files. RDA files can be loaded with e.g. load("asia.rda"), which creates an object called bn in the current scope. RDS files can be loaded with e.g. asia = readRDS("asia.rda"), which returns the network and assigns it.

Discrete Bayesian Networks

Small Networks (<20 nodes)
Name Nodes Arcs Parameters
ASIA8818
CANCER5410
EARTHQUAKE5410
SACHS1117178
SURVEY6621
Medium Networks (20–50 nodes)
Name Nodes Arcs Parameters
ALARM3746509
BARLEY4884114005
CHILD2025230
INSURANCE2752984
MILDEW3546540150
WATER326610083
Large Networks (50–100 nodes)
Name Nodes Arcs Parameters
HAILFINDER56662656
HEPAR II701231453
WIN95PTS76112574
Very Large Networks (100–1000 nodes)
Name Nodes Arcs Parameters
ANDES2233381157
DIABETES413602429409
LINK724112514211
MUNIN (4 subnetworks)186–1041273–138815622–80352
PATHFINDER13520077155
PIGS4415925618
Massive Networks (>1000 nodes)
Name Nodes Arcs Parameters
MUNIN (full network)1041139780592
MUNIN (4 subnetworks)186–1041273–138815622–80352

Gaussian Bayesian Networks

Medium Networks (20–50 nodes)
Name Nodes Arcs Parameters
MAGIC-NIAB4466110
Large Networks (50–100 nodes)
Name Nodes Arcs Parameters
MAGIC-IRRI64102166