Journal article
Hyper Markov laws in the statistical analysis of decomposable graphical models
- Abstract:
-
This paper introduces and investigates the notion of a hyper Markov law, which is a probability distribution over the set of probability measures on a multivariate space that (i) is concentrated on the set of Markov probabilities over some decomposable graph, and (ii) satisfies certain conditional independence restrictions related to that graph. A stronger version of this hyper Markov property is also studied. Our analysis starts by reconsidering the properties of Markov probabilities, using ...
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- Publication status:
- Published
- Peer review status:
- Peer reviewed
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Bibliographic Details
- Publisher:
- Institute of Mathematical Statistics Publisher's website
- Journal:
- Annals of Statistics Journal website
- Volume:
- 21
- Issue:
- 3
- Pages:
- 1272-1317
- Publication date:
- 1993-09-01
- ISSN:
-
00905364
Item Description
- Language:
- English
- Keywords:
- Subjects:
- UUID:
-
uuid:11092582-70ab-409d-aa64-c2ddd35ade57
- Local pid:
- ora:1956
- Deposit date:
- 2008-05-20
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- Copyright holder:
- Institute of Mathematical Statistics
- Copyright date:
- 1993
- Notes:
- N. B. Professor Lauritzen was based at Aarlborg University when this article was first published. The full-text of this article is not available in ORA. Citation: Dawid, A. P. & Lauritzen, S. L. (1993). 'Hyper Markov laws in the statistical analysis of decomposable graphical models', Annals of Statistics, 21(3), 1272-1317. [Available at http://www.imstat.org/aos/].
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