Application of referenced thermodynamic integration to Bayesian model selection

Evaluating normalising constants is important across a range of topics in statistical learning, notably Bayesian model selection. However, in many realistic problems this involves the integration of analytically intractable, high-dimensional distributions, and therefore requires the use of stochasti...

Full description

Saved in:
Bibliographic Details
Published in:PloS one 2023-08, Vol.18 (8), p.e0289889-e0289889
Main Authors: Hawryluk, Iwona, Mishra, Swapnil, Flaxman, Seth, Bhatt, Samir, Mellan, Thomas A
Format: Article
Language:English
Subjects:
Bayes Theorem
Bayesian analysis
Bayesian information criterion
COVID-19
Density
Dimensional analysis
Humans
Hypotheses
Integrals
Integration
Machine learning
Mathematical models
Medicine and Health Sciences
Methods
Modelling
People and places
Physical Sciences
Republic of Korea
Research and analysis methods
Stochastic methods
Stochasticity
Thermodynamics
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Evaluating normalising constants is important across a range of topics in statistical learning, notably Bayesian model selection. However, in many realistic problems this involves the integration of analytically intractable, high-dimensional distributions, and therefore requires the use of stochastic methods such as thermodynamic integration (TI). In this paper we apply a simple but under-appreciated variation of the TI method, here referred to as referenced TI, which computes a single model's normalising constant in an efficient way by using a judiciously chosen reference density. The advantages of the approach and theoretical considerations are set out, along with pedagogical 1 and 2D examples. The approach is shown to be useful in practice when applied to a real problem -to perform model selection for a semi-mechanistic hierarchical Bayesian model of COVID-19 transmission in South Korea involving the integration of a 200D density.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0289889