Island filters for inference on metapopulation dynamics | Vidéo | Carmin.tv

00:00:00 / 00:00:00

Island filters for inference on metapopulation dynamics

By Edward Ionides

Appears in collection : Thematic Month Week 3: Mathematical Modeling and Statistical Analysis of Infectious Disease Outbreaks / Mois thématique Semaine 3 : Modélisation mathématique et analyses statistique des épidémies de maladies infectieuses

Low-dimensional compartment models for biological systems can be fitted to time series data using Monte Carlo particle filter methods. As dimension increases, for example when analyzing a collection of spatially coupled populations, particle filter methods rapidly degenerate. We show that many independent Monte Carlo calculations, each of which does not attempt to solve the filtering problem, can be combined to give a global filtering solution with favorable theoretical scaling properties under a weak coupling condition. The independent Monte Carlo calculations are called islands, and the operation carried out on each island is called adapted simulation, so the complete algorithm is called an adapted simulation island filter. We demonstrate this methodology and some related algorithms on a model for measles transmission within and between cities.

Information about the video

Date of recording 17/02/2020
Date of publication 03/03/2020
Institution CIRM
Licence CC BY NC ND
Language English
Audience Researchers
Director(s) Guillaume Hennenfent
Format MP4

Citation data

DOI 10.24350/CIRM.V.19612403
Cite this video Ionides, Edward (17/02/2020). Island filters for inference on metapopulation dynamics. CIRM. Audiovisual resource. DOI: 10.24350/CIRM.V.19612403
URL https://dx.doi.org/10.24350/CIRM.V.19612403

Domain(s)

Bibliography

IONIDES, Edward L., ASFAW, Kidus, PARK, Joonha, et al. Island filters for partially observed spatiotemporal systems. arXiv preprint arXiv:2002.05211, 2020. - https://arxiv.org/abs/2002.05211
DEL MORAL, Pierre et MURRAY, Lawrence M. Sequential Monte Carlo with highly informative observations. SIAM/ASA Journal on Uncertainty Quantification, 2015, vol. 3, no 1, p. 969-997. - https://doi.org/10.1137/15M1011214
PARK, Joonha et IONIDES, Edward L. A guided intermediate resampling particle filter for inference on high dimensional systems. arXiv preprint arXiv:1708.08543, 2017. - https://arxiv.org/abs/1708.08543
SHEPARD, N. et PITT, M. K. Filtering via simulation: auxiliary particle filter. Journal of the American Statistical Association, 1999, vol. 94, p. 590-599. - http://dx.doi.org/10.2307/2670179
King, A. A., Nguyen, D. and Ionides, E. L. (2016). Statistical inference for partially observed Markov processes via the R package pomp, Journal of Statistical Software 69: 1–43. - http://dx.doi.org/10.18637/jss.v069.i12
Asfaw, K., Ionides, E. L. and King, A. A. (2019). spatPomp: R package for statistical inference for spatiotemporal partially observed Markov processes, - [https: //github.com/kidusasfaw/spatPomp](https: //github.com/kidusasfaw/spatPomp)
IONIDES, Edward L., NGUYEN, Dao, ATCHADÉ, Yves, et al. Inference for dynamic and latent variable models via iterated, perturbed Bayes maps. Proceedings of the National Academy of Sciences, 2015, vol. 112, no 3, p. 719-724. - https://doi.org/10.1073/pnas.1410597112
ANDRIEU, Christophe, DOUCET, Arnaud, et HOLENSTEIN, Roman. Particle markov chain monte carlo methods. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2010, vol. 72, no 3, p. 269-342. - https://doi.org/10.1111/j.1467-9868.2009.00736.xISTEX

MSC codes

Last related questions on MathOverflow

You have to connect your Carmin.tv account with mathoverflow to add question

Ask a question on MathOverflow

Copyright Carmin.tv 2025

Give feedback