The IMAP hybrid method for learning gaussian bayes nets

Authors:
Oliver Schulte;Gustavo Frigo;Russell Greiner;Hassan Khosravi
Affiliations:
School of Computing Science, Simon Fraser University, Burnaby, B.C., Canada;School of Computing Science, Simon Fraser University, Burnaby, B.C., Canada;Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada;School of Computing Science, Simon Fraser University, Burnaby, B.C., Canada
Venue:
AI'10 Proceedings of the 23rd Canadian conference on Advances in Artificial Intelligence
Year:
2010

Citing 12
Cited 0

Probabilistic reasoning in intelligent systems: networks of plausible inference

Probabilistic reasoning in intelligent systems: networks of plausible inference
Adaptive Probabilistic Networks with Hidden Variables

Machine Learning - Special issue on learning with probabilistic representations
A tutorial on learning with Bayesian networks

Learning in graphical models
Model Selection Criteria for Learning Belief Nets: An Empirical Comparison

ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Optimal structure identification with greedy search

The Journal of Machine Learning Research
The max-min hill-climbing Bayesian network structure learning algorithm

Machine Learning
Learning Bayesian Networks

Learning Bayesian Networks
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)

Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
A Scoring Function for Learning Bayesian Networks based on Mutual Information and Conditional Independence Tests

The Journal of Machine Learning Research
Learning graphical model structure using L1-regularization paths

AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Mind change optimal learning of Bayes net structure

COLT'07 Proceedings of the 20th annual conference on Learning theory
Learning bayesian network structure from massive datasets: the «sparse candidate« algorithm

UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents the I-map hybrid algorithm for selecting, given a data sample, a linear Gaussian model whose structure is a directed graph The algorithm performs a local search for a model that meets the following criteria: (1) The Markov blankets in the model should be consistent with dependency information from statistical tests (2) Minimize the number of edges subject to the first constraint (3) Maximize a given score function subject to the first two constraints Our local search is based on Graph Equivalence Search (GES); we also apply the recently developed SIN statistical testing strategy to help avoid local minima Simulation studies with GES search and the BIC score provide evidence that for nets with 10 or more variables, the hybrid method selects simpler graphs whose structure is closer to the target graph.