Evaluating Anytime Algorithms for Learning Optimal Bayesian Networks

by   Brandon Malone, et al.

Exact algorithms for learning Bayesian networks guarantee to find provably optimal networks. However, they may fail in difficult learning tasks due to limited time or memory. In this research we adapt several anytime heuristic search-based algorithms to learn Bayesian networks. These algorithms find high-quality solutions quickly, and continually improve the incumbent solution or prove its optimality before resources are exhausted. Empirical results show that the anytime window A* algorithm usually finds higher-quality, often optimal, networks more quickly than other approaches. The results also show that, surprisingly, while generating networks with few parents per variable are structurally simpler, they are harder to learn than complex generating networks with more parents per variable.



page 1

page 2

page 3

page 4


Comments on: "Hybrid Semiparametric Bayesian Networks"

Invited discussion on the paper "Hybrid Semiparametric Bayesian Networks...

From Incomplete, Dynamic Data to Bayesian Networks

Bayesian networks are a versatile and powerful tool to model complex phe...

An Improved Admissible Heuristic for Learning Optimal Bayesian Networks

Recently two search algorithms, A* and breadth-first branch and bound (B...

Most Relevant Explanation: Properties, Algorithms, and Evaluations

Most Relevant Explanation (MRE) is a method for finding multivariate exp...

Parallel Sampling for Efficient High-dimensional Bayesian Network Structure Learning

Score-based algorithms that learn the structure of Bayesian networks can...

Learning Bayesian Networks from Incomplete Data with Stochastic Search Algorithms

This paper describes stochastic search approaches, including a new stoch...

Exploiting Qualitative Knowledge in the Learning of Conditional Probabilities of Bayesian Networks

Algorithms for learning the conditional probabilities of Bayesian networ...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.