DeepAI AI Chat
Log In Sign Up

A Traveling Salesman Learns Bayesian Networks

by   Tuhin Sahai, et al.

Structure learning of Bayesian networks is an important problem that arises in numerous machine learning applications. In this work, we present a novel approach for learning the structure of Bayesian networks using the solution of an appropriately constructed traveling salesman problem. In our approach, one computes an optimal ordering (partially ordered set) of random variables using methods for the traveling salesman problem. This ordering significantly reduces the search space for the subsequent greedy optimization that computes the final structure of the Bayesian network. We demonstrate our approach of learning Bayesian networks on real world census and weather datasets. In both cases, we demonstrate that the approach very accurately captures dependencies between random variables. We check the accuracy of the predictions based on independent studies in both application domains.


page 1

page 2

page 3

page 4


Inference in Graded Bayesian Networks

Machine learning provides algorithms that can learn from data and make i...

Structure Learning for Hybrid Bayesian Networks

Bayesian networks have been used as a mechanism to represent the joint d...

Scalable Exact Parent Sets Identification in Bayesian Networks Learning with Apache Spark

In Machine Learning, the parent set identification problem is to find a ...

Multi-objective optimization to explicitly account for model complexity when learning Bayesian Networks

Bayesian Networks have been widely used in the last decades in many fiel...

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

Algorithms for learning the conditional probabilities of Bayesian networ...

Learning Bayesian Networks from Ordinal Data

Bayesian networks are a powerful framework for studying the dependency s...

Fast Counting in Machine Learning Applications

We propose scalable methods to execute counting queries in machine learn...