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Bayesian Inference for Multiple Datasets

Author

Listed:
  • Renata Retkute

    (Epidemiology and Modelling Group, Department of Plant Sciences, University of Cambridge, Downing Street, Cambridge CB2 3EA, UK)

  • William Thurston

    (Met Office, Fitzroy Road, Exeter EX1 3PB, UK)

  • Christopher A. Gilligan

    (Epidemiology and Modelling Group, Department of Plant Sciences, University of Cambridge, Downing Street, Cambridge CB2 3EA, UK)

Abstract

Estimating parameters for multiple datasets can be time consuming, especially when the number of datasets is large. One solution is to sample from multiple datasets simultaneously using Bayesian methods such as adaptive multiple importance sampling (AMIS). Here, we use the AMIS approach to fit a von Mises distribution to multiple datasets for wind trajectories derived from a Lagrangian Particle Dispersion Model driven from 3D meteorological data. A posterior distribution of parameters can help to characterise the uncertainties in wind trajectories in a form that can be used as inputs for predictive models of wind-dispersed insect pests and the pathogens of agricultural crops for use in evaluating risk and in planning mitigation actions. The novelty of our study is in testing the performance of the method on a very large number of datasets (>11,000). Our results show that AMIS can significantly improve the efficiency of parameter inference for multiple datasets.

Suggested Citation

  • Renata Retkute & William Thurston & Christopher A. Gilligan, 2024. "Bayesian Inference for Multiple Datasets," Stats, MDPI, vol. 7(2), pages 1-11, May.
    • Handle: RePEc:gam:jstats:v:7:y:2024:i:2:p:26-444:d:1391582
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    References listed on IDEAS

    as
    1. Tapio Schneider & Tobias Bischoff & Gerald H. Haug, 2014. "Migrations and dynamics of the intertropical convergence zone," Nature, Nature, vol. 513(7516), pages 45-53, September.
    2. Víctor Elvira & Luca Martino & Christian P. Robert, 2022. "Rethinking the Effective Sample Size," International Statistical Review, International Statistical Institute, vol. 90(3), pages 525-550, December.
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