A0600
Title: FROSTY: A high-dimensional scale-free Bayesian network learning method
Authors: Sang-Yun Oh - University of California, Santa Barbara (United States) [presenting]
Abstract: A scalable Bayesian network learning algorithm is proposed based on sparse Cholesky decomposition. The approach only requires observational data and user-specified confidence levels as inputs and can estimate networks with thousands of variables. The computational complexity of the proposed method is $O(p^3)$ for a graph with p vertices. Extensive numerical experiments illustrate the usefulness of the method with promising results. In simulations, the initial step in the approach also improves an alternative Bayesian network structure estimation method that uses an undirected graph as an input.
