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Mathematical datasets, statistical benchmarks, probability, optimization, operations research
2,469 datasets
56-node Bayesian network designed for forecasting severe weather, specifically hail. Hailfinder was authored by B. Abramson, J. Brown, W. Edwards, A. Murphy, and R. L. Winkler and published in the International Journal of Forecasting in 1996. The network structure contains 66 arcs and 2656 parameters, modeling probabilistic relationships between meteorological variables.
413 nodes and 602 arcs define this discrete Bayesian network for modeling diabetes. The model, authored by S. Andreassen, R. Hovorka, J. Benn, K. G. Olesen, and E. R. Carson, was presented in a 1991 conference on artificial intelligence in medicine. It contains 429,409 parameters and has an average Markov blanket size of 3.97.
Insurance_4 is a discrete Bayesian network sample from the bnlearn repository, designed for modeling insurance-related variables. The network contains 27 nodes, 52 arcs, and 1008 parameters, with an average Markov blanket size of 5.19. It was authored by J. Binder, D. Koller, S. Russell, and K. Kanazawa, with a foundational citation from their 1997 Machine Learning paper on adaptive probabilistic networks.
27 nodes and 52 arcs define this discrete Bayesian network for insurance applications. The network contains 1008 parameters and was created by J. Binder, D. Koller, S. Russell, and K. Kanazawa, with a reference publication from 1997. It is part of the bnlearn Bayesian Network Repository's discrete-medium collection.
A sample from the Insurance Bayesian Network, a probabilistic model with 27 nodes and 52 arcs. The dataset contains 1008 parameters and was created by J. Binder, D. Koller, S. Russell, and K. Kanazawa, referenced in their 1997 Machine Learning publication.
Hailfinder_1 is a Bayesian network model for forecasting severe weather, containing 56 nodes and 66 arcs. The model was authored by B. Abramson, J. Brown, W. Edwards, A. Murphy, and R. L. Winkler and published in the International Journal of Forecasting in 1996. It has 2656 parameters and an average Markov blanket size of 3.54.
Hailfinder is a Bayesian network model for forecasting severe weather, developed by B. Abramson, J. Brown, W. Edwards, A. Murphy, and R. L. Winkler. The model contains 56 nodes and 66 arcs, representing 2656 parameters. It was published in the International Journal of Forecasting in 1996.
Hailfinder Bayesian Network is a probabilistic model for forecasting severe weather. The network contains 56 nodes and 66 arcs, with 2656 parameters. It was authored by B. Abramson, J. Brown, W. Edwards, A. Murphy, and R. L. Winkler and published in 1996.
A Bayesian network with 56 nodes and 66 arcs, containing 2656 parameters for probabilistic weather forecasting. The model was authored by B. Abramson, J. Brown, W. Edwards, A. Murphy, and R. L. Winkler and is referenced in their 1996 paper 'Hailfinder: A Bayesian system for forecasting severe weather'.
56 nodes and 66 arcs define the Hailfinder Bayesian network, a probabilistic model for forecasting severe weather. The network contains 2656 parameters and was authored by B. Abramson, J. Brown, W. Edwards, A. Murphy, and R. L. Winkler, with research published in the International Journal of Forecasting in 1996.
109 nodes and 195 arcs define the Pathfinder Bayesian network, a probabilistic model for medical diagnosis. The network, authored by D. Heckerman, E. Horwitz, and B. Nathwani, contains 72,079 parameters and was published in a 1992 research paper. It represents a foundational expert system for reasoning under uncertainty in pathology.
Diabetes_1 is a discrete Bayesian network model for diabetes, referenced from the bnlearn repository. The model contains 413 nodes connected by 602 arcs, with 429,409 parameters. It was authored by S. Andreassen, R. Hovorka, J. Benn, K. G. Olesen, and E. R. Carson, with a related paper published in 1991.
413 nodes and 602 arcs define a discrete Bayesian network for modeling diabetes. The network contains 429,409 parameters and was developed by S. Andreassen, R. Hovorka, J. Benn, K. G. Olesen, and E. R. Carson, with a related paper published in 1991.
A Bayesian network model for diabetes, referenced from the bnlearn repository. The model contains 413 nodes, 602 arcs, and 429,409 parameters, with an average Markov blanket size of 3.97. It was authored by S. Andreassen, R. Hovorka, J. Benn, K. G. Olesen, and E. R. Carson for a model-based approach to insulin adjustment, presented in 1991.
A Bayesian network model for diabetes with 413 nodes, 602 arcs, and 429,409 parameters. The model was developed by S. Andreassen, R. Hovorka, J. Benn, K. G. Olesen, and E. R. Carson. It was presented in a 1991 conference paper titled 'A Model-based Approach to Insulin Adjustment'.
A Bayesian network model for diabetes management, containing 413 nodes and 602 arcs. The model was developed by S. Andreassen, R. Hovorka, J. Benn, K. G. Olesen, and E. R. Carson and presented at the 3rd Conference on Artificial Intelligence in Medicine in 1991. It has 429,409 parameters and is designed for a model-based approach to insulin adjustment.
A sample from the Pathfinder Bayesian Network, a discrete, very-large network used as a machine learning benchmark. The network contains 109 nodes, 195 arcs, and 72,079 parameters, with an average Markov blanket size of 3.82. It was authored by D. Heckerman, E. Horwitz, and B. Nathwani, based on research published in 1992.
A Bayesian network model for diabetes with 413 nodes and 602 arcs, containing 429,409 parameters. The model was developed by S. Andreassen, R. Hovorka, J. Benn, K. G. Olesen, and E. R. Carson. The work was presented at the 3rd Conference on Artificial Intelligence in Medicine in 1991.
413 nodes and 602 arcs form a Bayesian network modeling diabetes-related variables. The model was created by S. Andreassen, R. Hovorka, J. Benn, K. G. Olesen, and E. R. Carson for a model-based approach to insulin adjustment, as presented in a 1991 conference proceedings. It contains 429,409 parameters and has an average Markov blanket size of 3.97.
413 nodes and 602 arcs define this Bayesian network modeling diabetes. The network contains 429,409 parameters and was authored by S. Andreassen, R. Hovorka, J. Benn, K. G. Olesen, and E. R. Carson for a model-based approach to insulin adjustment, published in 1991.