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.
CITATION STYLE
Chopin, N., & Gerber, M. (2018). Sequential Quasi-Monte Carlo: Introduction for non-experts, dimension reduction, application to partly observed diffusion processes. In Springer Proceedings in Mathematics and Statistics (Vol. 241, pp. 99–121). Springer New York LLC. https://doi.org/10.1007/978-3-319-91436-7_5
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