Learning Bayes net structure from sparse data sets

Learning Bayes net structure from sparse data sets (2001)

by Kevin P. Murphy

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Datum	Value	Source
TITLE	Learning Bayes net structure from sparse data sets	SVM HeaderParse 0.1
AUTHOR NAME	Kevin P. Murphy	SVM HeaderParse 0.1
ABSTRACT	There are essentially two kinds of approaches for learning the structure of Bayesian Networks (BNs) from data. The first approach tries to find a graph which satis es all the constraints implied by the empirical conditional independencies measured in the data [PV91, SGS00a, Shi00]. The second approach searches through the space of models (either DAGs or PDAGs), and uses some scoring metric (typically Bayesian or some approximation, such as BIC/MDL) to evaluate the models [CH92, Hec95, Hec98, Kra98], typically returning the highest scoring model found. Our main interest is in learning BN structure from gene expression data [FLNP00, HGJY01, MM99, SGS00b]. In domains such as this, where the ratio of the number of observations to the number of variables is low (i.e., when we have sparse data), selecting a threshold for the conditional independence (CI) tests can be tricky, and repeated use of such tests can lead to inconsistencies [DD99]. Bayesian s...	user correction - Legacy Corrections
YEAR	2001	INFERENCE
VENUE TYPE	TECHREPORT	INFERENCE
TECH	Technical report	INFERENCE
CITATIONS	65 found	ParsCit 1.0