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bnlearn — bnlearn Bayesian network repository (1988-2005)

Description

Directed acyclic graphs of reference (discrete) Bayesian networks commonly used as benchmarks for Bayesian network learning and inference, as collected in the Bayesian Network Repository of the bnlearn R package by M. Scutari. Most of these networks were constructed by domain experts as probabilistic expert systems, and many were previously distributed by the Bayesian Network Repository of the Hebrew University of Jerusalem. Nodes are random variables, and a directed edge from A to B means that A is a parent of B, i.e. that the probability distribution of B is specified conditionally on A. The networks in bnlearn have been checked and fixed where needed (e.g. so that the conditional probability tables sum to one, and without dummy single-state nodes or dependencies on nonexistent nodes), so their sizes may differ from other versions of the same networks.

The networks are:

  • asia: a small fictitious model relating lung diseases, visits to Asia, smoking, X-ray results and dyspnoea (Lauritzen and Spiegelhalter, 1988).
  • cancer and earthquake: small textbook examples from Korb and Nicholson, "Bayesian Artificial Intelligence" (2010).
  • sachs: a causal protein-signaling network in human immune system cells, inferred from flow cytometry data (Sachs et al., 2005).
  • survey: a textbook example on the usage of different means of transport, from Scutari and Denis, "Bayesian Networks: with Examples in R" (2021).
  • alarm: the ALARM (A Logical Alarm Reduction Mechanism) system for patient monitoring in intensive care (Beinlich et al., 1989).
  • barley: a preliminary model for a decision support system for growing malting barley without pesticides (Kristensen and Rasmussen, 2002).
  • child: a diagnostic system for congenital heart disease in newborn babies ("blue babies") (Spiegelhalter and Cowell, 1992).
  • insurance: a network for estimating the expected claim costs of car insurance policyholders (Binder et al., 1997).
  • mildew: a model for the management of mildew in winter wheat (Jensen and Jensen, 1996).
  • water: an expert system for the control of waste water treatment (Jensen et al., 1989).
  • hailfinder: a system for forecasting severe weather in northeastern Colorado (Abramson et al., 1996).
  • hepar2: a system for diagnosis of liver disorders (Onisko, 2003).
  • win95pts: a system for troubleshooting printing problems in Microsoft Windows 95.
  • andes: a model of a student's knowledge in the ANDES intelligent tutoring system for Newtonian physics (Conati et al., 1997).
  • diabetes: a model of carbohydrate metabolism used for insulin dose adjustment (Andreassen et al., 1991).
  • link: a model for linkage analysis in genetics, over a large pedigree (Jensen and Kong, 1999).
  • munin, munin1, munin2, munin3, munin4: the MUNIN expert system for electromyography (Andreassen et al., 1989), as the full network and four subnetworks.
  • pathfinder: the PATHFINDER expert system for the diagnosis of lymph-node diseases (Heckerman et al., 1992).
  • pigs: a pedigree model for the breeding of pigs.

The networks have the following properties:

  • Nodes: name, the name of the variable; n_states, the number of values (states) the discrete variable can take; states, the names of the states.
Tags
Informational Unweighted Metadata
Citation
  • M. Scutari, "Learning Bayesian Networks with the bnlearn R Package." Journal of Statistical Software 35(3), 1-22 (2010) 10.18637/jss.v035.i03 Sci-Hub
  • S. Lauritzen, D. Spiegelhalter, "Local Computations with Probabilities on Graphical Structures and Their Application to Expert Systems." Journal of the Royal Statistical Society: Series B 50(2), 157-194 (1988) 10.1111/j.2517-6161.1988.tb01721.x Sci-Hub
  • K. Sachs, O. Perez, D. Pe'er, D. A. Lauffenburger, G. P. Nolan, "Causal Protein-Signaling Networks Derived from Multiparameter Single-Cell Data." Science 308, 523-529 (2005) 10.1126/science.1105809 Sci-Hub
  • I. A. Beinlich, H. J. Suermondt, R. M. Chavez, G. F. Cooper, "The ALARM Monitoring System: A Case Study with two Probabilistic Inference Techniques for Belief Networks." AIME 89, Lecture Notes in Medical Informatics 38, 247-256 (1989) 10.1007/978-3-642-93437-7_28 Sci-Hub
  • K. Kristensen, I. A. Rasmussen, "The use of a Bayesian network in the design of a decision support system for growing malting barley without use of pesticides." Computers and Electronics in Agriculture 33(3), 197-217 (2002) 10.1016/S0168-1699(02)00007-8 Sci-Hub
  • D. J. Spiegelhalter, R. G. Cowell, "Learning in probabilistic expert systems." In Bayesian Statistics 4, 447-466, Clarendon Press (1992) www.bnlearn.com
  • J. Binder, D. Koller, S. Russell, K. Kanazawa, "Adaptive Probabilistic Networks with Hidden Variables." Machine Learning 29, 213-244 (1997) 10.1023/A:1007421730016 Sci-Hub
  • A. L. Jensen, F. V. Jensen, "MIDAS - An Influence Diagram for Management of Mildew in Winter Wheat." Proceedings of the Twelfth Conference on Uncertainty in Artificial Intelligence (UAI 1996), 349-356 (1996) arXiv:1302.3587
  • F. V. Jensen, U. Kjærulff, K. G. Olesen, J. Pedersen, "Et Forprojekt Til et Ekspertsystem for Drift af Spildevandsrensning (An Expert System for Control of Waste Water Treatment - A Pilot Project)." Technical Report, Judex Datasystemer A/S, Aalborg (1989) www.bnlearn.com
  • B. Abramson, J. Brown, W. Edwards, A. Murphy, R. L. Winkler, "Hailfinder: A Bayesian system for forecasting severe weather." International Journal of Forecasting 12(1), 57-71 (1996) 10.1016/0169-2070(95)00664-8 Sci-Hub
  • A. Onisko, "Probabilistic Causal Models in Medicine: Application to Diagnosis of Liver Disorders." Ph.D. Dissertation, Institute of Biocybernetics and Biomedical Engineering, Polish Academy of Science, Warsaw (2003) www.bnlearn.com
  • C. Conati, A. S. Gertner, K. VanLehn, M. J. Druzdzel, "On-Line Student Modeling for Coached Problem Solving Using Bayesian Networks." User Modeling, 231-242 (1997) 10.1007/978-3-7091-2670-7_24 Sci-Hub
  • S. Andreassen, R. Hovorka, J. Benn, K. G. Olesen, E. R. Carson, "A Model-Based Approach to Insulin Adjustment." AIME 91, Lecture Notes in Medical Informatics 44, 239-248 (1991) 10.1007/978-3-642-48650-0_19 Sci-Hub
  • C. S. Jensen, A. Kong, "Blocking Gibbs Sampling for Linkage Analysis in Large Pedigrees with Many Loops." The American Journal of Human Genetics 65(3), 885-901 (1999) 10.1086/302524 Sci-Hub
  • S. Andreassen, F. V. Jensen, S. K. Andersen, B. Falck, U. Kjærulff, M. Woldbye, A. R. Sørensen, A. Rosenfalck, F. Jensen, "MUNIN - an Expert EMG Assistant." In Computer-Aided Electromyography and Expert Systems, Chapter 21, Elsevier (1989) www.bnlearn.com
  • D. Heckerman, E. Horvitz, B. Nathwani, "Toward Normative Expert Systems: Part I The Pathfinder Project." Methods of Information in Medicine 31(2), 90-105 (1992) 10.1055/s-0038-1634867 Sci-Hub
Upstream URL OK
https://www.bnlearn.com/bnrepository/
Networks
Tip: click on a column header to sort the list, or hover over it for a description.
Column legend
$\left<k\right>$
Mean degree
$\sigma_k$
Standard deviation of the degrees
$\lambda_h$
Largest eigenvalue of the Hashimoto (non-backtracking) matrix
$\tau$
Random walk mixing time
$r$
Degree assortativity
$c$
Global clustering coefficient
$\oslash$
(Pseudo-) diameter
$S$
Relative size of the largest component
NPs
Node properties
EPs
Edge properties
Name Nodes Edges $\left<k\right>$ $\sigma_k$ $\lambda_h$ $\tau$ $r$ $c$ $\oslash$ $S$ Kind Mode NPs EPs gt GraphML GML csv
asia 8 8 1.00 0.87 1.00 3.50 -0.40 0.00 4 1.00 Directed Unipartite name n_states states 3 KiB 3 KiB 3 KiB 3 KiB
cancer 5 4 0.80 1.20 1.00 0.03 -1.00 0.00 2 1.00 Directed Bipartite name n_states states 3 KiB 3 KiB 3 KiB 3 KiB
earthquake 5 4 0.80 1.20 1.00 0.03 -1.00 0.00 2 1.00 Directed Bipartite name n_states states 3 KiB 3 KiB 3 KiB 3 KiB
sachs 11 17 1.55 1.56 2.83 1.25 -0.20 0.55 2 0.73 Directed Unipartite name n_states states 3 KiB 3 KiB 3 KiB 3 KiB
survey 6 6 1.00 1.00 1.00 1.44 -0.73 0.00 3 1.00 Directed Bipartite name n_states states 3 KiB 3 KiB 3 KiB 3 KiB
alarm 37 46 1.24 1.33 2.20 43.03 -0.05 0.06 12 1.00 Directed Unipartite name n_states states 3 KiB 4 KiB 4 KiB 4 KiB
barley 48 84 1.75 1.74 3.71 18.69 0.14 0.11 8 1.00 Directed Unipartite name n_states states 5 KiB 5 KiB 5 KiB 5 KiB
child 20 25 1.25 1.66 2.00 4.62 -0.23 0.05 5 1.00 Directed Unipartite name n_states states 3 KiB 4 KiB 3 KiB 4 KiB
insurance 27 52 1.93 1.99 3.60 4.48 -0.26 0.13 5 1.00 Directed Unipartite name n_states states 4 KiB 4 KiB 4 KiB 4 KiB
mildew 35 46 1.31 1.51 2.26 12.10 -0.16 0.00 8 1.00 Directed Bipartite name n_states states 4 KiB 4 KiB 4 KiB 4 KiB
water 32 66 2.06 1.87 3.91 5.95 -0.16 0.00 7 1.00 Directed Bipartite name n_states states 3 KiB 4 KiB 3 KiB 4 KiB
hailfinder 56 66 1.18 2.38 2.19 37.38 -0.39 0.04 10 1.00 Directed Unipartite name n_states states 4 KiB 5 KiB 4 KiB 4 KiB
hepar2 70 123 1.76 3.83 4.95 8.35 -0.48 0.04 6 1.00 Directed Unipartite name n_states states 4 KiB 5 KiB 4 KiB 4 KiB
win95pts 76 112 1.47 2.00 2.97 18.77 -0.10 0.05 7 1.00 Directed Unipartite name n_states states 4 KiB 5 KiB 4 KiB 4 KiB
andes 223 338 1.52 1.86 3.39 43.57 0.00 0.04 14 0.99 Directed Unipartite name n_states states 4 KiB 6 KiB 5 KiB 5 KiB
diabetes 413 602 1.46 1.71 2.70 87.75 0.01 0.04 17 1.00 Directed Unipartite name n_states states 5 KiB 7 KiB 7 KiB 8 KiB
pathfinder 109 195 1.79 10.11 6.89 3.84 -0.43 0.05 4 1.00 Directed Unipartite name n_states states 5 KiB 6 KiB 6 KiB 6 KiB
pigs 441 592 1.34 2.57 3.07 61.74 -0.06 0.00 19 1.00 Directed Unipartite name n_states states 6 KiB 8 KiB 7 KiB 8 KiB
munin 1,041 1,397 1.34 3.12 7.70 92.99 -0.05 0.00 16 1.00 Directed Unipartite name n_states states 10 KiB 15 KiB 14 KiB 15 KiB
munin1 186 273 1.47 2.15 3.64 42.53 -0.14 0.00 14 1.00 Directed Unipartite name n_states states 6 KiB 7 KiB 6 KiB 6 KiB
munin2 1,003 1,244 1.24 1.99 3.54 481.73 -0.13 0.00 30 1.00 Directed Unipartite name n_states states 9 KiB 14 KiB 14 KiB 15 KiB
munin3 1,041 1,306 1.25 3.06 7.43 89.97 -0.06 0.00 18 1.00 Directed Unipartite name n_states states 10 KiB 15 KiB 14 KiB 15 KiB
munin4 1,038 1,388 1.34 3.01 7.43 92.99 -0.05 0.00 16 1.00 Directed Unipartite name n_states states 10 KiB 15 KiB 14 KiB 15 KiB