Poisson Approximate Likelihood versus the block particle filter for a spatiotemporal measles model
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866915385736953856 |
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| author | He, Kunyang Hao, Yize Ionides, Edward L. |
| author_facet | He, Kunyang Hao, Yize Ionides, Edward L. |
| contents | Filtering algorithms for high-dimensional nonlinear non-Gaussian partially observed stochastic processes provide access to the likelihood function and hence enable likelihood-based or Bayesian inference for this methodologically challenging class of models. A novel Poisson approximate likelihood (PAL) filter was introduced by Whitehouse et al.\ (2023). PAL employs a Poisson approximation to conditional densities, offering a fast approximation to the likelihood function for a certain subset of partially observed Markov process models. PAL was demonstrated on an epidemiological metapopulation model for measles, specifically, a spatiotemporal model for disease transmission within and between cities. At face value, Table\ 3 of Whitehouse et al.\ (2023) suggests that PAL considerably out-performs previous analysis as well as an ARMA benchmark model. We show that PAL does not outperform a block particle filter and that the lookahead component of PAL was implemented in a way that introduces substantial positive bias in the log-likelihood estimates. Therefore, the results of Table\ 3 of Whitehouse et al.\ (2023) do not accurately represent the true capabilities of PAL. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_09121 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Poisson Approximate Likelihood versus the block particle filter for a spatiotemporal measles model He, Kunyang Hao, Yize Ionides, Edward L. Methodology Filtering algorithms for high-dimensional nonlinear non-Gaussian partially observed stochastic processes provide access to the likelihood function and hence enable likelihood-based or Bayesian inference for this methodologically challenging class of models. A novel Poisson approximate likelihood (PAL) filter was introduced by Whitehouse et al.\ (2023). PAL employs a Poisson approximation to conditional densities, offering a fast approximation to the likelihood function for a certain subset of partially observed Markov process models. PAL was demonstrated on an epidemiological metapopulation model for measles, specifically, a spatiotemporal model for disease transmission within and between cities. At face value, Table\ 3 of Whitehouse et al.\ (2023) suggests that PAL considerably out-performs previous analysis as well as an ARMA benchmark model. We show that PAL does not outperform a block particle filter and that the lookahead component of PAL was implemented in a way that introduces substantial positive bias in the log-likelihood estimates. Therefore, the results of Table\ 3 of Whitehouse et al.\ (2023) do not accurately represent the true capabilities of PAL. |
| title | Poisson Approximate Likelihood versus the block particle filter for a spatiotemporal measles model |
| topic | Methodology |
| url | https://arxiv.org/abs/2507.09121 |