Corpus ID: 41901223

An Exploration of Structure Learning in Bayesian Networks

  title={An Exploration of Structure Learning in Bayesian Networks},
  • Published 2012
We start with a brief introduction to Bayesian networks. We provide an overview of learning Bayesian networks from data, and the different variations of this task. We then focus on the particular task of learning Bayesian network structure from fully observed data using a search-and-score approach. We discuss the Bayesian score and its implications, and survey the literature on existing structure-learning algorithms. We then develop two genetic algorithms for learning structure. The first… Expand
Bayesian network hybrid learning using an elite-guided genetic algorithm
An improved hybrid learning strategy that features parameterized genetic algorithms to learn the structure of BNs underlying a set of data samples that can efficiently prevail over other state-of-the-art structural learners on large networks is presented. Expand
An investigation of local patterns for estimation of distribution genetic programming
An improved estimation of distribution (EDA) genetic programming (GP) algorithm which does not rely upon a prototype tree and learns the distribution of ancestor node chains, "n-grams", in a fit fraction of each generation's population to create trees for the next generation. Expand
Introducing graphical models to analyze genetic programming dynamics
This work describes how to build an unbiased graphical model from a population of genetic programming trees and interprets the graphical models with respect to conventional knowledge about the influence of subtree crossover and mutation upon tree structure. Expand
Combining MDL and BIC to Build BNs for System Reliability Modeling
  • Xiaoping Zhong, W. You
  • Computer Science
  • 2015 2nd International Conference on Information Science and Security (ICISS)
  • 2015
A new method is proposed that combines the Minimal Description Length principle (MDL) and Bayesian Information Criterion (BIC) to partially overcome the weakness of traditional mothods and improve the accuracy of the reliability estimation results. Expand


Learning Bayesian Networks: Search Methods and Experimental Results
A metric for computing the relative posterior probability of a network structure given data developed by Heckerman et al. (1994a,b,c) has a property useful for inferring causation from data and is described. Expand
Bayesian Network Structure Learning from Limited Datasets through Graph Evolution
An evolutionary approach to the Bayesian structure learning problem, specifically tailored for learning sets of limited size, is proposed, able to outperform a state-of-the-art dependency-analysis algorithm, providing better models for small datasets. Expand
Structure Learning of Bayesian Networks by Genetic Algorithms: A Performance Analysis of Control Parameters
This work tackles the problem of the search for the best Bayesian network structure, given a database of cases, using the genetic algorithm philosophy for searching among alternative structures, by assuming an ordering between the nodes of the network structures. Expand
Ordering-Based Search: A Simple and Effective Algorithm for Learning Bayesian Networks
It is shown that ordering-based search outperforms the standard baseline, and is competitive with recent algorithms that are much harder to implement. Expand
Being Bayesian About Network Structure. A Bayesian Approach to Structure Discovery in Bayesian Networks
This paper shows how to efficiently compute a sum over the exponential number of networks that are consistent with a fixed order over network variables, and uses this result as the basis for an algorithm that approximates the Bayesian posterior of a feature. Expand
Learning Bayesian Network Structure from Massive Datasets: The "Sparse Candidate" Algorithm
An algorithm that achieves faster learning by restricting the search space, which restricts the parents of each variable to belong to a small subset of candidates and is evaluated both on synthetic and real-life data. Expand
Data mining of Bayesian networks using cooperative coevolution
A novel data mining algorithm that employs cooperative coevolution and a hybrid approach to discover Bayesian networks from data to improve learning effectiveness and efficiency is proposed and illustrates that the algorithm is a promising alternative to other data mining algorithms. Expand
Learning Bayesian networks in the space of structures by estimation of distribution algorithms
Two novel population‐based stochastic search approaches, univariate marginal distribution algorithm (UMDA) and population-based incremental learning (PBIL) are used to learn a Bayesian network structure from a database of cases in a score + search framework. Expand
The Bayesian Structural EM Algorithm
This paper extends Structural EM to deal directly with Bayesian model selection and proves the convergence of the resulting algorithm and shows how to apply it for learning a large class of probabilistic models, including Bayesian networks and some variants thereof. Expand
Construction of Bayesian network structures from data: A brief survey and an efficient algorithm
This work presents an algorithm that integrates CI tests to generate an ordering on the nodes from the database, which is then used to recover the underlying Bayesian network structure using a non-CI-test-based method. Expand