Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
A Bayesian Multiresolution Independence Test for Continuous Variables
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Distribution-Free Learning of Bayesian Network Structure
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Using Markov Blankets for Causal Structure Learning
The Journal of Machine Learning Research
Causality Discovery with Additive Disturbances: An Information-Theoretical Perspective
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Efficient and robust independence-based Markov network structure discovery
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Mining and visualising ordinal data with non-parametric continuous BBNs
Computational Statistics & Data Analysis
A partial correlation-based algorithm for causal structure discovery with continuous variables
IDA'07 Proceedings of the 7th international conference on Intelligent data analysis
A heuristic partial-correlation-based algorithm for causal relationship discovery on continuous data
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
A causal discovery algorithm using multiple regressions
Pattern Recognition Letters
Kernel-Based Hybrid Random Fields for Nonparametric Density Estimation
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Learning an L1-regularized Gaussian Bayesian network in the equivalence class space
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Learning Non-Stationary Dynamic Bayesian Networks
The Journal of Machine Learning Research
Learning bayesian networks from Markov random fields: An efficient algorithm for linear models
ACM Transactions on Knowledge Discovery from Data (TKDD)
Learning Bayesian network structure using Markov blanket decomposition
Pattern Recognition Letters
A PC algorithm variation for ordinal variables
Computational Statistics
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In this paper we present a method for learning the structure of Bayesian networks (BNs) without making any assumptions on the probability distribution of the domain. This is mainly useful for continuous domains, where there is little guidance and many choices for the parametric distribution families to be used for the local conditional probabilities of the Bayesian network, and only a few have been examined analytically. We therefore focus on BN structure learning in continuous domains. We address the problem by developing a conditional independence test for continuous variables, which can be readily used by any existing independence-based BN structure learning algorithm. Our test is non-parametric, making no assumptions on the distribution of the domain. We also provide an effective and computationally efficient method for calculating it from data. We demonstrate the learning of the structure of graphical models in continuous domains from real-world data, to our knowledge for the first time using independence-based methods and without distributional assumptions. We also experimentally show that our test compares favorably with existing statistical approaches which use prediscretization, and verify desirable properties such as statistical consistency.