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Sequential quasi-Monte Carlo, new applications

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The objective of this talk is twofold. First, I would like to present SQMC (Sequen-
tial quasi-Monte Carlo), a class of algorithms that merges particle filtering and QMC.
Contrary to previous presentations, I will not assume prior knowledge from the audience
regarding particle filtering, state-space modelling and Feynmac-Kac representations. I will thus
take to introduce these notions and the motivation to perform particle filtering.
Second, I would like to discuss some recent extensions and applications of SQMC, in
particular to partly observed diffusion models, which are infinitely-dimensional. QMC
techniques, and particularly SQMC, tend to suffer from a curse of dimensionality: their
performance gain, relative to Monte Carlo tends to vanish for large-dimensional prob-
lems. However, by exploiting well-known properties of partly observed diffusion models,
we are able to implement SQMC so that it outperforms significantly standard particle filtering.

Tuesday, September 6, 2016 - 14:00 to 16:00
Inria Lille - Nord Europe, bâtiment A, salle plénière
Nicolas Chopin