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Application of referenced thermodynamic integration to Bayesian model selection.
- Source :
-
PLoS ONE . 8/14/2023, Vol. 18 Issue 8, p1-16. 16p. - Publication Year :
- 2023
-
Abstract
- 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. [ABSTRACT FROM AUTHOR]
- Subjects :
- *STATISTICAL learning
*COVID-19
Subjects
Details
- Language :
- English
- ISSN :
- 19326203
- Volume :
- 18
- Issue :
- 8
- Database :
- Academic Search Index
- Journal :
- PLoS ONE
- Publication Type :
- Academic Journal
- Accession number :
- 169947494
- Full Text :
- https://doi.org/10.1371/journal.pone.0289889