Browse/search for people

Publication - Dr Mathieu Gerber

    Sequential Quasi-Monte Carlo: Introduction for Non-experts, Dimension Reduction, Application to Partly Observed Diffusion Processes

    Citation

    Chopin, N & Gerber, M, 2018, ‘Sequential Quasi-Monte Carlo: Introduction for Non-experts, Dimension Reduction, Application to Partly Observed Diffusion Processes’. in: Monte Carlo and Quasi-Monte Carlo Methods: MCQMC 2016, Stanford, CA, August 14-19. Springer International Publishing AG

    Abstract

    SMC (Sequential Monte Carlo) is a class of Monte Carlo algorithms for filtering and related sequential problems. Gerber and Chopin (J R Stat Soc Ser B Stat Methodol 77(3):509–579, 2015, [16]) introduced SQMC (Sequential quasi-Monte Carlo), a QMC version of SMC. This paper has two objectives: (a) to introduce Sequential Monte Carlo to the QMC community, whose members are usually less familiar with state-space models and particle filtering; (b) to extend SQMC to the filtering of continuous-time state-space models, where the latent process is a diffusion. A recurring point in the paper will be the notion of dimension reduction, that is how to implement SQMC in such a way that it provides good performance despite the high dimension of the problem.

    Full details in the University publications repository