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See detailDiscussing the validation of high-dimensional probability distribution learning with mixtures of graphical models for inference
Schnitzler, François ULg

Poster (2010, October 06)

Exact inference on probabilistic graphical models quickly becomes intractable when the dimension of the problem increases. A weighted average (or mixture) of different simple graphical models can be used ... [more ▼]

Exact inference on probabilistic graphical models quickly becomes intractable when the dimension of the problem increases. A weighted average (or mixture) of different simple graphical models can be used instead of a more complicated model to learn a distribution, allowing probabilistic inference to be much more efficient. I hope to discuss issues related to the validation of algorithms for learning such mixtures of models and to high-dimensional learning of probabilistic graphical models in general, and to gather valuable feedback and comments on my approach. The main problems are the difficulties to assess the accuracy of the algorithms and to choose a representative set of target distributions. The accuracy of algorithms for learning probabilistic graphical models is often evaluated by comparing the structure of the resulting model to the target (e.g. Number of similar/dissimilar edges, score BDe etc). This approach however falls short when studying methods using a mixture of simple models : individually, these lack the representation power to model the true distribution, and only their combination allows them to compete with more sophisticated models. The Kullback-Leibler divergence is a measure of the difference between two probability densities, and can be used to compare any model learned from a dataset to the data generating distribution. For computational reasons, I however had to resort to a Monte Carlo estimation of this quantity for large problems (starting at around 200 variables). Since probabilistic inference is the ultimate motivation for building these models, and not probability modelling, a more meaningful measure of accuracy could be obtained by comparing mixtures against a combination of state of the art model learning and approximate inference algorithms. However, the exact inference result cannot be easily assessed for interesting target distributions, since the use of mixtures is precisely considered because exact inference is not possible on said targets, and approximate inference would introduce a bias. Selecting a target distribution used to generate the data sets on which the algorithms are evaluated also proved a challenge. The easiest solution was to generate them at random (although different approaches can be designed). These models are however likely to be rather different from real problems, and thus constitute a poor choice to assess the practical interest of mixture of models. Methods (e.g. linking multiple copies of a given network) have been developed to increase the size of models known by the community (e.g. the alarm network), and the obtained graphical models have been made available. These could however still be far from the kind of interactions present in a real setting. A better way to proceed could be to generate samples based on the equations describing a physical problem, to learn a probabilistic model as best as possible from this high-dimensional dataset, and to use it as target distribution. [less ▲]

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See detailDiscussion
Richelle, Marc ULg

in Angelergue, André (Ed.) Psychologie de la connaissance de soi (1975)

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in Berr, Henri (Ed.) Le ciel dans l'histoire et la science : exposés de la huitième semaine internationale de synthèse (1936)

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Peer Reviewed
See detailDiscussion : Pratiques évaluatives en classe et épreuves externes
Lafontaine, Dominique ULg

in Congrès international. Actualité de al recherche en éducation et en formation. AREF 2010 (2010)

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See detailLa discussion à visée démocratique et philosophique au prisme de la critique deleuzienne de la discussion. Analyse d'un verbatim.
Herla, Anne ULg; Jeanmart, Gaëlle

in Diotime. Revue internationale de didactique de la philosophie. (2014, April), 60

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WANG, François-Charles ULg

Conference (2014, October 04)

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Scientific conference (2013, September 19)

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See detailDiscussion de la communication faite par M. Demoor, membre titulaire, sous le titre: "Le mécanisme du rythme cardiaque".
Fredericq, Léon ULg

in Bulletin de l'Académie Royale de Médecine de Belgique (1880)

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See detailDiscussion des rapports
Richelle, Marc ULg

in La genèse de la parole : 16e symposium de l'Association de psychologie scientifique de langue française (1977)

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See detailDiscussion des rapports de P. Fraisse et R. Chauvin
Richelle, Marc ULg

in Canestrelli, I. (Ed.) Le comportement : symposium de l'Association de psychologie de langue française, Rome, 1967 (1968)

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Peer Reviewed
See detailDiscussion du symposium intitulé : "L’étude des pratiques d’évaluation sommative des enseignants : une urgence pour comprendre les compétences professionnelles en jeu ? "
Fagnant, Annick ULg

Conference (2016, January)

Symposium organisé par Lucie Mottier Lopez et Raphaël Pasquini.

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Peer Reviewed
See detailDiscussion of "Sensitivity analysis of non-equilibrium adaptation parameters for modeling mining-pit migration"
Gouverneur, Ludovic ULg; Dewals, Benjamin ULg; Archambeau, Pierre ULg et al

in Journal of Hydraulic Engineering (2013), 139(7), 799-801

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