ArticleScientific reports2024
Bayesian active learning with model selection for spectral experiments.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Authors and funding
5 authors.
Funding
Abstract
Active learning is a common approach to improve the efficiency of spectral experiments. Model selection from the candidates and parameter estimation are often required in the analysis of spectral experiments. Therefore, we proposed an active learning with model selection method using multiple parametric models as learning models. Important points for model selection and its parameter estimation were actively measured using Bayesian posterior distribution. The present study demonstrated the effectiveness of our proposed method for spectral deconvolution and Hamiltonian selection in X-ray photoelectron spectroscopy.
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Registered trials
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