On the Laplace Approximation as Model Selection Criterion for Gaussian Processes Article Swipe
Andreas Besginow
,
Jan David Hüwel
,
Thomas Pawellek
,
Christian Beecks
,
Markus Lange‐Hegermann
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2403.09215
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2403.09215
Model selection aims to find the best model in terms of accuracy, interpretability or simplicity, preferably all at once. In this work, we focus on evaluating model performance of Gaussian process models, i.e. finding a metric that provides the best trade-off between all those criteria. While previous work considers metrics like the likelihood, AIC or dynamic nested sampling, they either lack performance or have significant runtime issues, which severely limits applicability. We address these challenges by introducing multiple metrics based on the Laplace approximation, where we overcome a severe inconsistency occuring during naive application of the Laplace approximation. Experiments show that our metrics are comparable in quality to the gold standard dynamic nested sampling without compromising for computational speed. Our model selection criteria allow significantly faster and high quality model selection of Gaussian process models.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2403.09215
- https://arxiv.org/pdf/2403.09215
- OA Status
- green
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4392871506
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4392871506Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2403.09215Digital Object Identifier
- Title
-
On the Laplace Approximation as Model Selection Criterion for Gaussian ProcessesWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-03-14Full publication date if available
- Authors
-
Andreas Besginow, Jan David Hüwel, Thomas Pawellek, Christian Beecks, Markus Lange‐HegermannList of authors in order
- Landing page
-
https://arxiv.org/abs/2403.09215Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2403.09215Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2403.09215Direct OA link when available
- Concepts
-
Laplace transform, Applied mathematics, Selection (genetic algorithm), Laplace's method, Model selection, Mathematics, Gaussian, Gaussian process, Statistical physics, Mathematical optimization, Statistics, Computer science, Mathematical analysis, Physics, Artificial intelligence, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1Per-year citation counts (last 5 years)
- Related works (count)
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10Other works algorithmically related by OpenAlex
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