# Learning Bayesian Networks with Low Rank Conditional Probability Tables

@article{Barik2019LearningBN, title={Learning Bayesian Networks with Low Rank Conditional Probability Tables}, author={Adarsh Barik and Jean Honorio}, journal={ArXiv}, year={2019}, volume={abs/1905.12552} }

In this paper, we provide a method to learn the directed structure of a Bayesian network using data. The data is accessed by making conditional probability queries to a black-box model. We introduce a notion of simplicity of representation of conditional probability tables for the nodes in the Bayesian network, that we call "low rankness". We connect this notion to the Fourier transformation of real valued set functions and propose a method which learns the exact directed structure of a `low…

## 3 Citations

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