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4th Workshop on Algorithmic issues for Inference in Graphical Models - AIGM14 (2014)| shared_uid | 1815 |
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| title | 4th Workshop on Algorithmic issues for Inference in Graphical Models - AIGM14 |
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| type | Atelier |
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| year | 2014 |
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| start_date | 2014/09/22 |
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| stop_date | 2014/09/22 |
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| schedule | 09h |
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| active | no |
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| website | http://carlit.toulouse.inra.fr/wikiz/index.php/AIGM14 |
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| organisational_info | Organisation committee: S. de Givry, N. Peyrard, R. Sabbadin, T. Schiex, M. Vignes (MIA-T, INRA Toulouse, France) and S. Robin (AgroParisTech, Paris, France). |
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| summary | Most real (e.g. biological) complex systems are formed or modelled by elementary objects that locally interact with each other. Local properties can often be measured, assessed or partially observed. On the other hand, global properties that stem from these local interactions are difficult to comprehend. It is now acknowledged that a mathematical modelling is an adequate framework to understand, to be able to control or to predict the behaviour of complex systems, such as gene regulatory networks or contact networks in epidemiology.
More precisely, graphical models (GM), which are formed by variables bound to their interactors by deterministic or stochastic relationships, allow researchers to model possibly high-dimensional heterogeneous data and to capture uncertainty. Analysis, optimal control, inference or prediction about complex systems benefit from the formalisation proposed by GM. To achieve such tasks, a key factor is to be able to answer general queries: what is the probability to observe such event in this situation ? Which model best represents my data ? What is the most acceptable solution to a query of interest that satisfies a list of given constraints ? Often, an exact resolution cannot be achieved either because of computational limits, or because of the intractability of the problem.
Objective
The aim of this workshop is to bridge the gap between Statistics and Artificial Intelligence communities where approximate inference methods for GM are developped. We are primarily interested in algorithmic aspects of probabilistic (e.g. Markov random fields, Bayesian networks, influence diagrams), deterministic (e.g. Constraint Satisfaction Problems, SAT, weighted variants, Generalized Additive Independence models) or hybrid (e.g. Markov logic networks) models. |
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| responsibles | Peyrard, de Givry, Robin |
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