Retkute, R., Thurston, W., & Gilligan, C.A. (2024). Bayesian Inference for Multiple Datasets. Preprints. https://doi.org/10.20944/preprints202404.0146.v1
Chicago/Turabian Style
Retkute, R., William Thurston and Christopher A. Gilligan. 2024 "Bayesian Inference for Multiple Datasets" Preprints. https://doi.org/10.20944/preprints202404.0146.v1
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 Misses distribution to multiple datasets for wind trajectories derived from a Lagrangian Particle Dispersion Model driven from a 3D meteorological data. The primary objective is 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 pathogens of agricultural crops for use in evaluating risk and in planning mitigation actions. Our results show that AMIS can significantly improve the efficiency of parameter inference for multiple datasets.
Computer Science and Mathematics, Probability and Statistics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
