Application of referenced thermodynamic integration to Bayesian model selection Article Swipe
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· 2023
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pone.0289889
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.
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- article
- Language
- en
- Landing Page
- https://doi.org/10.1371/journal.pone.0289889
- https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0289889&type=printable
- OA Status
- gold
- References
- 44
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4385802197
Raw OpenAlex JSON
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https://openalex.org/W4385802197Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1371/journal.pone.0289889Digital Object Identifier
- Title
-
Application of referenced thermodynamic integration to Bayesian model selectionWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
-
2023-08-14Full publication date if available
- Authors
-
Iwona Hawryluk, Swapnil Mishra, Seth Flaxman, Samir Bhatt, Thomas A. MellanList of authors in order
- Landing page
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https://doi.org/10.1371/journal.pone.0289889Publisher landing page
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https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0289889&type=printableDirect link to full text PDF
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0289889&type=printableDirect OA link when available
- Concepts
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Bayesian probability, Selection (genetic algorithm), Model selection, Computational biology, Computer science, Biology, Artificial intelligenceTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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44Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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