@inproceedings{a7788d064bc64c7dae86e489c9da4ff2,
title = "Sequential Quasi-Monte Carlo: Introduction for non-experts, dimension reduction, application to partly observed diffusion processes",
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.",
keywords = "Diffusion models, Particle filtering, Randomised quasi-Monte Carlo, Sequential Monte Carlo, State-space models",
author = "Nicolas Chopin and Mathieu Gerber",
note = "Publisher Copyright: {\textcopyright} 2018, Springer International Publishing AG, part of Springer Nature.; 12th International Conference on Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing, MCQMC 2016 ; Conference date: 14-08-2016 Through 19-08-2016",
year = "2018",
month = jan,
day = "1",
doi = "10.1007/978-3-319-91436-7\_5",
language = "English",
isbn = "9783319914350",
series = "Springer Proceedings in Mathematics and Statistics",
publisher = "Springer New York LLC",
pages = "99--121",
editor = "Glynn, \{Peter W.\} and Owen, \{Art B.\}",
booktitle = "Monte Carlo and Quasi-Monte Carlo Methods - MCQMC 2016",
}