# Learning Bayesian Networks: Search Methods and Experimental Results

@inproceedings{Chickering1995LearningBN, title={Learning Bayesian Networks: Search Methods and Experimental Results}, author={Max Chickering and Dan Geiger and David Heckerman}, year={1995} }

We discuss Bayesian approaches for learning Bayesian networks from data. First, we review a metric for computing the relative posterior probability of a network structure given data developed by Heckerman et al. (1994a,b,c). We see that the metric has a property useful for inferring causation from data. Next, we describe search methods for identifying network structures with high posterior probabilities. We describe polynomial algorithms for finding the highestscoring network structures in the…

## 261 Citations

### Learning Bayesian Networks: The Combination of Knowledge and Statistical Data

- Computer ScienceMachine Learning
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A methodology for assessing informative priors needed for learning Bayesian networks from a combination of prior knowledge and statistical data is developed and how to compute the relative posterior probabilities of network structures given data is shown.

### An Exploration of Structure Learning in Bayesian Networks

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### Exact Bayesian Structure Discovery in Bayesian Networks

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### A Tutorial on Learning with Bayesian Networks

- Computer ScienceInnovations in Bayesian Networks
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Methods for constructing Bayesian networks from prior knowledge are discussed and methods for using data to improve these models are summarized, including techniques for learning with incomplete data.

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- 2002

This article provides an overview of how to handle uncertainty about which Bayesian network to use for calculating the effect of an ideal manipulation or a classification, and how searching over equivalence classes of Bayesian networks, instead of searching over Bayesian Networks, can simplify both scoring and search.

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- Mathematics
- 2012

Bayesian Networks are probabilistic graphical models that encode in a compact manner the conditional probabilistic relations over a set of random variables.
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### A GENETIC ALGORITHM FOR LEARNING BAYESIAN NETWORK ADJACENCY MATRICES FROM DATA

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This thesis describes the research with structure learning using a genetic algorithm to search the space of adjacency matrices for a Bayesian network, and evaluates the genetic algorithm using well-known networks, and shows that it is an effective structure-learning algorithm.

### Structural learning of Bayesian networks from complete data using the scatter search documents

- Computer Science2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583)
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The scatter search optimization algorithm is utilized in learning the structure of the Bayesian network from complete data through a heuristic search for the best network structure that maximizes a scoring function given a database of cases.

### Improving High-Dimensional Bayesian Network Structure Learning by Exploiting Search Space Information Technical Report 06-49

- Computer Science
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Model-Based Search (MBS) is presented, showing that MBS performs better than hill climbing in the Max-Min Parents and Children (MMPC) search space and can find better high-dimensional network structures than other leading structure learning algorithms.

### Learning Bayesian networks: approaches and issues

- Computer ScienceThe Knowledge Engineering Review
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This work takes a broad look at the literature on learning Bayesian networks—in particular their structure—from data, and hopes that all the major fields in the area are covered.

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