DOAJ Open Access 2022

Inference, Prediction, & Entropy-Rate Estimation of Continuous-Time, Discrete-Event Processes

Sarah E. Marzen James P. Crutchfield

Abstrak

Inferring models, predicting the future, and estimating the entropy rate of discrete-time, discrete-event processes is well-worn ground. However, a much broader class of discrete-event processes operates in continuous-time. Here, we provide new methods for inferring, predicting, and estimating them. The methods rely on an extension of Bayesian structural inference that takes advantage of neural network’s universal approximation power. Based on experiments with complex synthetic data, the methods are competitive with the state-of-the-art for prediction and entropy-rate estimation.

Topik & Kata Kunci

Science Astrophysics Physics

Penulis (2)

Sarah E. Marzen

James P. Crutchfield

Format Sitasi

APA MLA BibTeX

Marzen, S.E., Crutchfield, J.P. (2022). Inference, Prediction, & Entropy-Rate Estimation of Continuous-Time, Discrete-Event Processes. https://doi.org/10.3390/e24111675

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Informasi Jurnal

Tahun Terbit: 2022
Sumber Database: DOAJ
DOI: 10.3390/e24111675
Akses: Open Access ✓