Application of referenced thermodynamic integration to Bayesian model selection

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningfagfællebedømt


  • Fulltext

    Forlagets udgivne version, 1,24 MB, PDF-dokument

  • Iwona Hawryluk
  • Swapnil Mishra
  • Seth Flaxman
  • Bhatt, Samir
  • Thomas A. Mellan
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.
TidsskriftPLoS ONE
Udgave nummer8
Antal sider16
StatusUdgivet - 2023

Bibliografisk note

Publisher Copyright:
Copyright: © 2023 Hawryluk et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Antal downloads er baseret på statistik fra Google Scholar og

Ingen data tilgængelig

ID: 363350038