100 samples of 5000 instances of categorical BNs from bnlearn's Bayesian Network Repository

We provide 100 samples, each containing 5000 instances, of discrete Bayesian Networks from bnlearn's Bayesian Network Repository. Specifically, the BNs, along with their characteristics, are:

NETWORK
#NODES
#EDGES
#PARAMETERS
MAX. PARENTS
MEAN DEGREE

Cancer
5
4
10
2
2.00

Earthquake
5
4
10
2
2.00

Survey
6
6
21
2
2.00

Asia
8
8
18
2
2.00

Sachs
11
17
178
3
3.09

Child
20
25
230
4
2.50

Insurance
27
52
230
4
2.50

Water
32
66
10083
5
4.12

Mildew
35
46
540150
3
2.63

Alarm
37
46
509
5
2.49

Barley
48
84
114005
4
3.50

Hailfinder
56
66
2656
4
2.36

Hepar2
70
123
1453
6
3.51

Win95pts
76
112
574
7
2.95

Pathfinder
109
195
72079
5
3.58

Munin1
186
273
15622
3
2.94

Andes
223
338
1157
6
3.03

Diabetes
413
602
429409
2
2.92

Pigs
441
592
5618
2
2.68

Link
724
1125
14211
3
3.11

Munin2
1003
1244 
69431
3
2.48

Munin4
1038
1388
80352
3
2.67

Munin3
1041
1306
71059
3
2.51

Munin
1041
1397
80592
3
2.68

Each dataset is sampled using Python and the bnlearn package. The BN structure is loaded from the .bif file using bif = bnlearn.import_DAG(path), and samples are generated with bnlearn.sampling(bif, n=5000, methodtype='bayes'). Post-processing is then applied to replace the generated numerical values with those categorical specified in the .bif structure file.

Additionally, ten extra old database samples of most BNs can be found on OpenML.