4.6 Article

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

期刊

ENTROPY
卷 24, 期 11, 页码 -

出版社

MDPI
DOI: 10.3390/e24111675

关键词

Poisson process; renewal process; hidden semi-Markov process; hidden Markov chain; epsilon-machine; Shannon entropy rate; optimal predictor; minimal predictor

资金

  1. Templeton World Charity Foundation Diverse Intelligences grant [TWCF0570]
  2. Foundational Questions Institute and Fetzer Franklin Fund [FQXI-RFP-CPW-2007]
  3. U.S. Army Research Laboratory
  4. U.S. Army Research Office [W911NF-21-1-0048, W911NF-18-1-0028]
  5. U.S. Department of Energy [DE-SC0017324]
  6. Moore Foundation
  7. U.S. Department of Energy (DOE) [DE-SC0017324] Funding Source: U.S. Department of Energy (DOE)

向作者/读者索取更多资源

This paper proposes new methods for inferring, predicting, and estimating continuous-time discrete-event processes. The methods are based on an extension of Bayesian structural inference and utilize the universal approximation power of neural networks. Experimental results on complex synthetic data demonstrate that these methods are competitive with the state-of-the-art for prediction and entropy-rate estimation.
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.

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