Bayesian Graphical Models for Discrete Data
Abstract
Pendant plus d'un demi-siecle, les graphes ont ete utilises pour representer des modeles statistiques en analyse de donnees. En particulier, les graphes decrivant l'independance conditionnelle sont apparus comme une classe importante de ces modeles. Des applications en analyse d'image, analyse de pedigree, ou encore en systeme expert sont a l'origine de leur developpement, et plusieurs livres de synthese ont deja ete publies a ce sujet. Le developpement d'un cadre Bayesien de ces modeles est en revanche moins connu, et les applications en systemes experts ont motive la recherche dans ce domaine. La possibilite de construire des modeles capables de se remettre a jour au fur et a mesure que de nouvelles donnees sont disponibles est a l'origine d'un interet intense de la part de la communaute travaillant en intelligence artificielle. Cependent, leur application a une classe plus vaste d'analyse de donnees a ete largement negligee. L'objet de cet article est de montrer comment les modeles Bayesiens de graphes permettent d'unifier et de simplifier des problemes standards, tels que les modeles log-lineaires Bayesiens (avec des donnees completes ou non), l'estimation d'une population fermee ou le double echantillonnage. Dans la mesure ou le choix d'un modele conventionel unique echoue dans ce type de situation, nous construisons des distributions a posteriori des quantites d'interet en moyennant sur les modeles possibles. Plus particulierement, nous introduisons la composition de ch…
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Alternative proportional hazards models that also fit the data well but lead to substantively different estimated effect sizes, standard errors, or predictions are commonplace.
“Models like M ∗∗ are commonplace: for striking examples see Regal and Hook (1991), Draper (1995), Madigan and York (1995), Kass and Raftery (1995), and Raftery (1996).”
From Bayesian model averaging: a tutorial (with comments by M. Clyde, David Draper and E. I. George, and a rejoinder by the authors (1999) · cites this paper as Madigan and York (1995)
For certain classes of models such as discrete graphical models and linear regression, closed form integrals for the marginal likelihood are available.
“For certain interesting classes of models suchas discrete graph ical models (e.g., Madigan and York, 1995) and linear regression (e.g., Raftery, Madigan and Hoeting, 1997), closed form integrals for the marginal likelihood, (3), are available.”
From Bayesian model averaging: a tutorial (with comments by M. Clyde, David Draper and E. I. George, and a rejoinder by the authors (1999) · cites this paper as Madigan and York, 1995
Madigan and York (1995) and York et al. (1995) provide extensive implementation details.
“Madigan and York (1995) and York et al. (1995) provide extensive implementation details.”
From Bayesian model averaging: a tutorial (with comments by M. Clyde, David Draper and E. I. George, and a rejoinder by the authors (1999) · cites this paper as Madigan and York (1995)
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- Authors
- Madigan, D., York, J., & Allard, D.
- Affiliations
- Carnegie Mellon University (US)
- University of Washington (US)
- Venue
- International Statistical Review / Revue Internationale de Statistique
- Publisher
- JSTOR
- Published
- 1995-08
- Volume
- 63
- Issue
- 2
- Pages
- 215
- ISSN
- 0306-7734 (ISSN-L)
- OpenAlex
- W2330192890
- Cited by
- 1,090
- References
- 2
- Citations by year
- 2026: 27 · 2025: 23 · 2024: 23 · 2023: 29 · 2022: 40 · 2021: 43 · 2020: 30 · 2019: 52 · 2018: 30 · 2017: 39 · 2016: 50 · 2015: 54 · 2014: 51 · 2013: 56 · 2012: 65
- FWCI
- 19.56
- Type
- journal article
- Language
- fr
- Open access
- closed
- Locations
- International Statistical Review (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
- Physical Sciences › Mathematics › Statistics and Probability › Markov Chains and Monte Carlo Methods
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Keywords
- Bayesian graphical models, conditional independence graphs, missing data, double sampling, posterior distribution, model averaging, Markov chain Monte Carlo, Bayesian networks, expert systems
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