Application of referenced thermodynamic integration to Bayesian model selection.
PLoS One
; 18(8): e0289889, 2023.
Article
em En
| MEDLINE
| ID: mdl-37578987
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.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
COVID-19
Tipo de estudo:
Prognostic_studies
Limite:
Humans
País/Região como assunto:
Asia
Idioma:
En
Revista:
PLoS One
Ano de publicação:
2023
Tipo de documento:
Article