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.

# Sequential quasi-Monte Carlo, new applications

Institutional tag:

Dates:

Tuesday, September 6, 2016 - 14:00 to 16:00

Location:

Inria Lille - Nord Europe, bâtiment A, salle plénière

Speaker(s):

Nicolas Chopin

Affiliation(s):

ENSAE, CREST

Speaker's URL: