Python package for Causal Discovery by learning the graphical structure of Bayesian networks. Structure Learning, Parameter Learning, Inferences, Sampling methods.
Learning Discrete Bayesian Networks from Continuous Data.
This paper introduces a principled Bayesian discretization method for continuous
variables in Bayesian networks with quadratic complexity instead of the cubic
complexity of other standard techniques. Empirical demonstrations show that the
proposed method is superior to the established minimum description length algorithm.
In addition, this paper shows how to incorporate existing methods into the structure
learning process to discretize all continuous variables and simultaneously learn
Bayesian network structures.
References
.. [1] Yi-Chun Chen, Tim Allan Wheeler, Mykel John Kochenderfer (2015),
Learning Discrete Bayesian Networks from Continuous Data :arxiv:1512.02406
Learning Discrete Bayesian Networks from Continuous Data.
This paper introduces a principled Bayesian discretization method for continuous variables in Bayesian networks with quadratic complexity instead of the cubic complexity of other standard techniques. Empirical demonstrations show that the proposed method is superior to the established minimum description length algorithm.
In addition, this paper shows how to incorporate existing methods into the structure learning process to discretize all continuous variables and simultaneously learn Bayesian network structures.
References
.. [1] Yi-Chun Chen, Tim Allan Wheeler, Mykel John Kochenderfer (2015), Learning Discrete Bayesian Networks from Continuous Data :arxiv:
1512.02406.. [2] Julia 0.4 implementation: https://github.com/sisl/LearnDiscreteBayesNets.jl