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Reversible Jump Markov Chain Monte Carlo for Pulse Fitting

Accepted version
Peer-reviewed

Type

Conference Object

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Authors

Goodyer, Fred 
Ahmad, Bashar I 

Abstract

This paper proposes a reversible jump Markov chain Monte Carlo method that provides efficient inference for the general problem of pulse fitting. In particular, it minimises the potential of an adopted parametric model overfitting to the (noisy) data via the inclusion of a peak proximity parameter. This facilitates learning a more representative underlying model and significantly reduces the computational cost. Synthetic and real data are used to demonstrate the efficacy of the introduced Bayesian technique.

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Keywords

Journal Title

ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Conference Name

ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Journal ISSN

1520-6149
2379-190X

Volume Title

Publisher

IEEE