التفاصيل البيبلوغرافية
| العنوان: |
Bayesian Inference for Multiple Datasets. |
| المؤلفون: |
Retkute, Renata, Thurston, William, Gilligan, Christopher A. |
| المصدر: |
Stats; Jun2024, Vol. 7 Issue 2, p434-444, 11p |
| مصطلحات موضوعية: |
BAYESIAN field theory, INSECT pathogens, CROPS, INSECT pests, PREDICTION models |
| مستخلص: |
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. [ABSTRACT FROM AUTHOR] |
|
Copyright of Stats is the property of MDPI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| قاعدة البيانات: |
Complementary Index |
Full Text Finder