# Evaluating Anytime Algorithms for Learning Optimal Bayesian Networks

@article{Malone2013EvaluatingAA, title={Evaluating Anytime Algorithms for Learning Optimal Bayesian Networks}, author={Brandon M. Malone and Changhe Yuan}, journal={ArXiv}, year={2013}, volume={abs/1309.6844} }

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…

## 25 Citations

### An Experimental Analysis of Anytime Algorithms for Bayesian Network Structure Learning

- Computer ScienceAMBN
- 2017

An extensive evaluation of the anytime behavior of the current state-of-the-art algorithms for Bayesian network structure learning (BNSL) finds that a local search algorithm based on memetic search dominates the performance of other state of theart algorithms when considering anytime behavior.

### Predicting the Hardness of Learning Bayesian Networks

- Computer ScienceAAAI
- 2014

The empirical results, based on the largest evaluation of state-of-the-art BNS learning algorithms to date, demonstrate that they can predict the runtimes to a reasonable degree of accuracy, and effectively select algorithms that perform well on a particular instance.

### Empirical hardness of finding optimal Bayesian network structures: algorithm selection and runtime prediction

- Computer ScienceMachine Learning
- 2017

It is shown that for a given solver the hardness of a problem instance can be efficiently predicted based on a collection of non-trivial features which go beyond the basic parameters of instance size, which enables effective selection of solvers that perform well in terms of runtimes on a particular instance.

### Finding Optimal Bayesian Network Structures with Constraints Learned from Data

- Computer ScienceUAI
- 2014

The observation that there is useful information implicit in the POPS is made, which shows that solving the constrained subproblems significantly improves the efficiency and scalability of heuristic search-based structure learning algorithms.

### An Improved Lower Bound for Bayesian Network Structure Learning

- Computer ScienceAAAI
- 2015

A new partition method based on information extracted from the potential optimal parent sets (POPS) of the variables of a data set can significantly improve the efficiency and scalability of heuristic search-based structure learning algorithms.

### Tightening Bounds for Bayesian Network Structure Learning

- Computer ScienceAAAI
- 2014

Methods for tightening the bounds of a breadth-first branch and bound algorithm by using more informed variablegroupings when creating the pattern databases and using an anytime learning algorithm are introduced.

### Learning Bayesian network structures under incremental construction curricula

- Computer ScienceNeurocomputing
- 2017

### Improved Local Search with Momentum for Bayesian Networks Structure Learning

- Computer ScienceEntropy
- 2021

This paper designs a framework, incorporating an influential perturbation factor integrated by three proposed operators, to escape current local optimal and improve the dilemma that outcomes trap in local optimal.

### Empirical Behavior of Bayesian Network Structure Learning Algorithms

- Computer ScienceAMBN@JSAI-isAI
- 2015

Empirical results show that machine learning techniques based on problem-dependent characteristics can often be used to accurately predict the algorithms' running times, and a comparison of exact and approximate search techniques is reviewed.

### Bidirectional heuristic search to find the optimal Bayesian network structure

- Computer Science, BusinessNeurocomputing
- 2021

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