Laurent Pierre

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The stochastic block model (SBM) is a probabilistic model designed to describe heterogeneous directed and undirected graphs. In this paper, we address the asymptotic inference in SBM by use of maximumlikelihood and variational approaches. The identifiability of SBM is proved while asymptotic properties of maximum-likelihood and variational estimators are(More)
The EMA (Energy Management Adviser) aims to produce personalised energy saving advice for EDF’s customers. The advice takes the form of one or more ‘tips’, and personalisation is achieved using semantic technologies: customers are described using RDF, an OWL ontology provides a conceptual model of the relevant domain (housing, environment, and so on) and(More)
The mixture model is a method of choice for modeling heterogeneous random graphs, because it contains most of the known structures of heterogeneity: hubs, hierarchical structures, or community structure. One of the weaknesses of mixture models on random graphs is that, at the present time, there is no computationally feasible estimation method that is(More)
The rational index ρL of a non-empty language L is a function of ℕ into ℕ, whose asymptotic behavior can be used to classify languages. We prove that the languages associated to Vector Addition System or Petri nets have rational indexes bounded by polynomials. This situation should be contrasted with the case of context-free languages. Indeed some(More)
Most research work in the field of automatic programming has been focused on conceptually complex problems. However, although most of the programs we are generally faced with may be very big and manage large volumes of data, they are conceptually simple. Starting from this consideration, we have developed, since 1992, a system called DESCARTES which, fully(More)