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David Maxwell Chickering
Researcher at Microsoft
Publications - 184
Citations - 17567
David Maxwell Chickering is an academic researcher from Microsoft. The author has contributed to research in topics: Bayesian network & Graphical model. The author has an hindex of 53, co-authored 184 publications receiving 16609 citations. Previous affiliations of David Maxwell Chickering include University of California, Los Angeles & University of California.
Papers
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Journal ArticleDOI
Learning Bayesian Networks: The Combination of Knowledge and Statistical Data
TL;DR: In this article, a Bayesian approach for learning Bayesian networks from a combination of prior knowledge and statistical data is presented, which is derived from a set of assumptions made previously as well as the assumption of likelihood equivalence, which says that data should not help to discriminate network structures that represent the same assertions of conditional independence.
Journal ArticleDOI
Optimal structure identification with greedy search
TL;DR: This paper proves the so-called "Meek Conjecture", which shows that if a DAG H is an independence map of another DAG G, then there exists a finite sequence of edge additions and covered edge reversals in G such that H remains anindependence map of G and after all modifications G =H.
Book ChapterDOI
Learning Bayesian Networks is NP-Complete
TL;DR: In this article, it was shown that the search problem of identifying a Bayesian network with a relative posterior probability greater than a given constant is NP-complete, when the BDe metric is used.
Journal ArticleDOI
Learning equivalence classes of bayesian-network structures
TL;DR: In this paper, the authors consider using a score equivalent criterion in conjunction with a heuristic search algorithm to perform model selection or model averaging, and show that more sophisticated search algorithms are likely to benefit much more.
Journal ArticleDOI
Large-Sample Learning of Bayesian Networks is NP-Hard
TL;DR: In this paper, it was shown that identifying high-scoring structures is NP-hard, even when any combination of one or more of the following holds: the generative distribution is perfect with respect to some DAG containing hidden variables; we are given an independence oracle; we were given an inference oracle, and we were also given an information oracle.