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dkesada
dbnR:Dynamic Bayesian Network Learning and Inference
Learning and inference over dynamic Bayesian networks of arbitrary Markovian order. Extends some of the functionality offered by the 'bnlearn' package to learn the networks from data and perform exact inference. It offers three structure learning algorithms for dynamic Bayesian networks: Trabelsi G. (2013) <doi:10.1007/978-3-642-41398-8_34>, Santos F.P. and Maciel C.D. (2014) <doi:10.1109/BRC.2014.6880957>, Quesada D., Bielza C. and Larrañaga P. (2021) <doi:10.1007/978-3-030-86271-8_14>. It also offers the possibility to perform forecasts of arbitrary length. A tool for visualizing the structure of the net is also provided via the 'visNetwork' package.
Maintained by David Quesada. Last updated 10 months ago.
bayesian-networksdynamic-bayesian-networksforecastinginferencetime-seriescpp
55 stars 5.01 score 37 scripts
robson-fernandes
dbnlearn:Dynamic Bayesian Network Structure Learning, Parameter Learning and Forecasting
It allows to learn the structure of univariate time series, learning parameters and forecasting. Implements a model of Dynamic Bayesian Networks with temporal windows, with collections of linear regressors for Gaussian nodes, based on the introductory texts of Korb and Nicholson (2010) <doi:10.1201/b10391> and Nagarajan, Scutari and Lèbre (2013) <doi:10.1007/978-1-4614-6446-4>.
Maintained by Robson Fernandes. Last updated 5 years ago.
bayesian-inferencebayesian-networksdynamic-bayesian-networksprobabilistic-graphical-modelstime-series
16 stars 4.32 score 26 scripts