Dear collegues,
The fourth edition of the AIGM workshop on Algorithmic Issues for
Inference in Graphical Models will be held in Paris, on the 22nd of
September 2014. The words "Graphical Models" here should be taken in a
broad sense, including both stochastic and deterministic graphical models.
Program and Web page: http://carlit.toulouse.inra.fr/wikiz/index.php/AIGM14
Thomas Schiex
INRA, Toulouse, France
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AIGM'14
Motivation
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.
The organisation committee:
S. de Givry, N. Peyrard, S. Robin, R. Sabbadin, T. Schiex, M. Vignes
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