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Island filters for inference on metapopulation dynamics

De Edward Ionides

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.

Informations sur la vidéo

Données de citation

  • DOI 10.24350/CIRM.V.19612403
  • Citer cette vidéo 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

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](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

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