A Weakly Informative Prior for Resonance Frequencies

Marnix Van Soom; Bart de Boer

doi:10.3390/psf2021003002

A Weakly Informative Prior for Resonance Frequencies

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Abstract

We derive a weakly informative prior for a set of ordered resonance frequencies from Jaynes’ principle of maximum entropy. The prior facilitates model selection problems in which both the number and the values of the resonance frequencies are unknown. It encodes a weakly inductive bias, provides a reasonable density everywhere, is easily parametrizable, and is easy to sample. We hope that this prior can enable the use of robust evidence-based methods for a new class of problems, even in the presence of multiplets of arbitrary order.

Original language	English
Number of pages	10
Journal	Physical Sciences Forum
Volume	3
Issue number	2
DOIs	https://doi.org/10.3390/psf2021003002
Publication status	Published - 4 Nov 2021
Event	MAXENT 2021: 40th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering - Graz University of Technology, Graz, Austria Duration: 4 Jul 2021 → 9 Jul 2021 https://www.tugraz.at/events/maxent2021/info/

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abstract = "We derive a weakly informative prior for a set of ordered resonance frequencies from Jaynes’ principle of maximum entropy. The prior facilitates model selection problems in which both the number and the values of the resonance frequencies are unknown. It encodes a weakly inductive bias, provides a reasonable density everywhere, is easily parametrizable, and is easy to sample. We hope that this prior can enable the use of robust evidence-based methods for a new class of problems, even in the presence of multiplets of arbitrary order.",

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AB - We derive a weakly informative prior for a set of ordered resonance frequencies from Jaynes’ principle of maximum entropy. The prior facilitates model selection problems in which both the number and the values of the resonance frequencies are unknown. It encodes a weakly inductive bias, provides a reasonable density everywhere, is easily parametrizable, and is easy to sample. We hope that this prior can enable the use of robust evidence-based methods for a new class of problems, even in the presence of multiplets of arbitrary order.

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A Weakly Informative Prior for Resonance Frequencies

Abstract

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