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Title: Comparison of two methods for approximation of probability distributions with prescribed marginals (English)
Author: Perez, Albert
Author: Studený, Milan
Language: English
Journal: Kybernetika
ISSN: 0023-5954
Volume: 43
Issue: 5
Year: 2007
Pages: 591-618
Summary lang: English
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Category: math
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Summary: Let $P$ be a discrete multidimensional probability distribution over a finite set of variables $N$ which is only partially specified by the requirement that it has prescribed given marginals $\lbrace P_{A};\ A\in {\cal S} \rbrace $, where ${\cal S}$ is a class of subsets of $N$ with $\bigcup {\cal S} = N$. The paper deals with the problem of approximating $P$ on the basis of those given marginals. The divergence of an approximation $\hat{P}$ from $P$ is measured by the relative entropy $H(P|\hat{P})$. Two methods for approximating $P$ are compared. One of them uses formerly introduced concept of dependence structure simplification (see Perez [Per79]). The other one is based on an explicit expression, which has to be normalized. We give examples showing that neither of these two methods is universally better than the other. If one of the considered approximations $\hat{P}$ really has the prescribed marginals then it appears to be the distribution $P$ with minimal possible multiinformation. A simple condition on the class ${\cal S}$ implying the existence of an approximation $\hat{P}$ with prescribed marginals is recalled. If the condition holds then both methods for approximating $P$ give the same result. (English)
Keyword: marginal problem
Keyword: relative entropy
Keyword: dependence structure simplification
Keyword: explicit expression approximation
Keyword: multiinformation
Keyword: decomposable model
Keyword: asteroid
MSC: 62C25
MSC: 62G05
MSC: 68T37
idZBL: Zbl 1144.68379
idMR: MR2376326
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Date available: 2009-09-24T20:27:33Z
Last updated: 2012-06-06
Stable URL: http://hdl.handle.net/10338.dmlcz/135801
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Reference: [1] Csiszár I., Matúš F.: Information projections revisited.IEEE Trans. Inform. Theory 49 (2003), 1474–1490 Zbl 1063.94016, MR 1984936
Reference: [2] Kellerer H. G.: Verteilungsfunktionen mit gegebenem Marginalverteilungen (in German, translation: Distribution functions with given marginal distributions).Z. Wahrsch. verw. Gerbiete 3 (1964), 247–270 MR 0175158
Reference: [3] Lauritzen S. L.: Graphical Models.Clarendon Press, Oxford 1996 Zbl 1055.62126, MR 1419991
Reference: [4] Perez A.: $\varepsilon $-admissible simplifications of the dependence structure of random variables.Kybernetika 13 (1979), 439–449 MR 0472224
Reference: [5] Perez A.: The barycenter concept of a set of probability measures as a tool in statistical decision.In: The book of abstracts of the 4th Internat. Vilnius Conference on Probability Theory and Mathematical Statistics 1985, pp. 226–228
Reference: [6] Perez A.: Princip maxima entropie a princip barycentra při integraci dílčích znalostí v expertních systémech (in Czech, translation: The maximum entropy principle and the barycenter principle in partial knowledge integration in expert systems).In: Metody umělé inteligence a expertní systémy III (V. Mařík and Z. Zdráhal, eds.), ČSVT – FEL ČVUT, Prague 1987, pp. 62–74
Reference: [7] Perez A.: Explicit expression Exe – containing the same multiinformation as that in the given marginal set – for approximating probability distributions.A manuscript in Word, 2003
Reference: [8] Studený M.: Pojem multiinformace v pravděpodobnostním rozhodování (in Czech, translation: The notion of multiinformation in probabilistic decision-making).CSc Thesis, Czechoslovak Academy of Sciences, Institute of Information Theory and Automation, Prague 1987
Reference: [9] Studený M.: Probabilistic Conditional Independence Structures.Springer–Verlag, London 2005
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