REVERSIBLE JUMP MARKOV CHAIN MONTE CARLO FOR PULSE FITTING

被引：0
作者：
Goodyer, Fred [1 ]
Ahmad, Bashar, I [1 ]
Godsill, Simon [1 ]
机构：
[1] Univ Cambridge, Dept Engn, Cambridge, England
来源：
2024 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP 2024) | 2024年
关键词：
MCMC; priors; peak fitting; overfitting; TDR; MODEL; MCMC;
D O I：
10.1109/ICASSP48485.2024.10448493
中图分类号：
O42 [声学];
学科分类号：
070206 ; 082403 ;
摘要：
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.
引用
收藏
页码：9556 / 9560
页数：5
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