Abstract
Bayesian inference shows that the distribution of the future event not only depends on the past events (prior), but also depends on the relation between the past and the future events (likelihood). However, the classical Bayesian methods do not consider the important contributions of recent data. In this paper, we propose a new Bayesian inference-based training method, which can be used as online training for Bayesian methods. We give the training methods for the exponential and the normal models. We successfully apply this method for the seismic parameter prediction using the data of central Italy from 2014 to 2017. Comparisons show our method is more effective than the other Bayesian methods.
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Funding
This work was supported in part by CONACYT under grant CONACyT-A1-S-8216 and in part by CINVESTAV under grant SEP-CINVESTAV #62 and Grant CNR-CINVESTAV.
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Morales, J., Yu, W. & Telesca, L. Bayesian inference for data-driven training with application to seismic parameter prediction. Soft Comput 26, 867–876 (2022). https://doi.org/10.1007/s00500-021-06232-z
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DOI: https://doi.org/10.1007/s00500-021-06232-z