@article{
 title = {A cross-validation package driving Netica with python},
 type = {article},
 year = {2015},
 keywords = {Bayesian networks,Cross-validation,Netica,Prediction,Probability,Python,Uncertainty},
 pages = {14-23},
 volume = {63},
 websites = {http://www.sciencedirect.com/science/article/pii/S1364815214002606},
 month = {1},
 id = {265f368a-fbca-3d7a-a9d5-ec0f95618aaa},
 created = {2015-04-11T19:52:27.000Z},
 accessed = {2015-04-11},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {71a29c65-85d2-3809-a3a1-fe4a94dc78d2},
 last_modified = {2017-03-14T14:27:45.955Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Bayesian networks (BNs) are powerful tools for probabilistically simulating natural systems and emulating process models. Cross validation is a technique to avoid overfitting resulting from overly complex BNs. Overfitting reduces predictive skill. Cross-validation for BNs is known but rarely implemented due partly to a lack of software tools designed to work with available BN packages. CVNetica is open-source, written in Python, and extends the Netica software package to perform cross-validation and read, rebuild, and learn BNs from data. Insights gained from cross-validation and implications on prediction versus description are illustrated with: a data-driven oceanographic application; and a model-emulation application. These examples show that overfitting occurs when BNs become more complex than allowed by supporting data and overfitting incurs computational costs as well as causing a reduction in prediction skill. CVNetica evaluates overfitting using several complexity metrics (we used level of discretization) and its impact on performance metrics (we used skill).},
 bibtype = {article},
 author = {Fienen, Michael N. and Plant, Nathaniel G.},
 doi = {10.1016/j.envsoft.2014.09.007},
 journal = {Environmental Modelling & Software}
}

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