• Corpus ID: 1011569

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… 

Figures and Tables from this paper

Learning Bayesian Networks: The Combination of Knowledge and Statistical Data

TLDR
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

  • Computer Science
  • 2012
TLDR
This work develops two genetic algorithms for learning structure from fully observed data using a search-and-score approach, and applies one of them to a structure-discovery task in the Genetic-Programming domain.

Exact Bayesian Structure Discovery in Bayesian Networks

TLDR
This work presents an algorithm that computes the exact posterior probability of a subnetwork, e.g., a directed edge, and shows that also in domains with a large number of variables, exact computation is feasible, given suitable a priori restrictions on the structures.

A Tutorial on Learning with Bayesian Networks

  • D. Heckerman
  • Computer Science
    Innovations in Bayesian Networks
  • 1998
TLDR
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.

Data mining tasks and methods: Probabilistic and casual networks: methodology for probabilistic networks

TLDR
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.

Structural learning of bayesian networks using statistical constraints

Bayesian Networks are probabilistic graphical models that encode in a compact manner the conditional probabilistic relations over a set of random variables. In this thesis we address the NP-complete

A GENETIC ALGORITHM FOR LEARNING BAYESIAN NETWORK ADJACENCY MATRICES FROM DATA

TLDR
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

TLDR
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

TLDR
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

TLDR
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.
...

References

SHOWING 1-10 OF 15 REFERENCES

Learning Gaussian Networks

Model Selection and Accounting for Model Uncertainty in Graphical Models Using Occam's Window

Abstract We consider the problem of model selection and accounting for model uncertainty in high-dimensional contingency tables, motivated by expert system applications. The approach most used

Sequential updating of conditional probabilities on directed graphical structures

TLDR
It is shown how one can introduce imprecision into such probabilities as a data base of cases accumulates and how to take advantage of a range of well-established statistical techniques.

Approximating discrete probability distributions with dependence trees

TLDR
It is shown that the procedure derived in this paper yields an approximation of a minimum difference in information when applied to empirical observations from an unknown distribution of tree dependence, and the procedure is the maximum-likelihood estimate of the distribution.

The ALARM Monitoring System: A Case Study with two Probabilistic Inference Techniques for Belief Networks

TLDR
Two algorithms were applied to this belief network: a message-passing algorithm by Pearl for probability updating in multiply connected networks using the method of conditioning and the Lauritzen-Spiegelhalter algorithm for local probability computations on graphical structures.

If tI

    If llli n &, | = L then s'(c;lII

      1) = d3 .d -rrl 2. If III;0^94,1 = {ri} then p(c; -7lq = 7) = 62.mt 3. If ltlinS,l= lrj,ry,) then p(x; = llri -1,tp = 1) = d