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Stochastic Models and Algorithms for Applications


At the end of this module, the student will have understood and be able to explain (main concepts) a series of statistical and probabilistic problems which are of interest for applications. We will focus in this course on :
· Partially observed models and associated problems (Hidden Markov chains,
Filtering, EM and SEM Algorithm,...)
· Stochastic Approximation and Applications (Optimal Quantization, Variance Reduction,...)
· Simulated Annealing and Hastings-Metropolis Algorithms
· Genetic Algorithms.
· Modelling of genes transmission and genealogical Trees (Wright-Fisher Model, Moran Model and Coalescent Processes)
. Urns Models and Applications (Ehrenfest,...)

Needed prerequisite

Probability and Statistics
Further lectures in probability

Form of assessment

The evaluation of outcome prior learning is made as a continuous training during the semester. According ot the teaching, the assessment will be different: as a written exam, an oral exam, a record, a written report, peers review...