English
Island filters for inference on metapopulation dynamics
Multi angle
Auteurs : Ionides, Edward (Auteur de la conférence)
CIRM (Editeur )
60G35 - Applications (signal detection, filtering, etc.), See Also { 62M20, 93E10, 93E11, 94Axx}
60J20 - Applications of Markov chains and discrete-time Markov processes on general state spaces
62M02 - Markov processes: hypothesis testing
62M05 - Markov processes: estimation
62M20 - Prediction; filtering (statistics)
62P10 - Applications of statistics to biology and medical sciences
65C35 - Stochastic particle methods (numerical analysis)
CIRM (Editeur )
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Résumé :
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.
Mots-Clés : Particle filter; sequential Monte Carlo; spatiotemporal data
Codes MSC : Mots-Clés : Particle filter; sequential Monte Carlo; spatiotemporal data
60G35 - Applications (signal detection, filtering, etc.), See Also { 62M20, 93E10, 93E11, 94Axx}
60J20 - Applications of Markov chains and discrete-time Markov processes on general state spaces
62M02 - Markov processes: hypothesis testing
62M05 - Markov processes: estimation
62M20 - Prediction; filtering (statistics)
62P10 - Applications of statistics to biology and medical sciences
65C35 - Stochastic particle methods (numerical analysis)
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Informations sur la Vidéo
Réalisateur : Hennenfent, GuillaumeLangue : Anglais Date de Publication : 03/03/2020 Date de Captation : 17/02/2020 Sous Collection : Research talks Catégorie arXiv : Quantitative Biology ; Methodology ; Probability Domaine(s) : Probabilités & Statistiques Format : MP4 (.mp4) - HD Durée : 00:39:24 Audience : Chercheurs Download : https://videos.cirm-math.fr/ 
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Informations sur la Rencontre
Nom de la Rencontre : 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 infectieusesOrganisateurs de la Rencontre : Britton, Tom ; Forien, Raphaël ; Hubert, Florence ; Pardoux, Etienne Dates : 17/02/2020 - 21/02/2020 Année de la rencontre : 2020 URL de la Rencontre : https://conferences.cirm-math.fr/2303.html
Données de citation
DOI : 10.24350/CIRM.V.19612403Citer cette vidéo: Ionides, Edward (2020). Island filters for inference on metapopulation dynamics. CIRM. Audiovisual resource. doi:10.24350/CIRM.V.19612403 URI : http://dx.doi.org/10.24350/CIRM.V.19612403 |
Voir Aussi
- [Multi angle] Interview at Cirm: Julia GOG / Personne interviewée Gog, julia.
- [Multi angle] Interview at Cirm: Tom Britton / Personne interviewée Britton, Tom.
- [Post-edited] Estimating parameters in the initial phase of an epidemic / Auteur de la conférence Scalia Tomba, Gianpaolo.
- [Multi angle] Near-criticality in mathematical models of epidemics / Auteur de la conférence Luczak, Malwina.
- [Multi angle] Evolutionary ecology of antibiotic resistance / Auteur de la conférence Lehtinen, Sonja.
- [Multi angle] Towards reproducibility and transparency in mathematical and computational epidemiology / Auteur de la conférence Frost, Simon.
Bibliographie
- 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
- 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
