Inference,Prediction, & Entropy-Rate Estimation of Continuous-Time,Discrete-Event Processes |
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Authors: | Sarah E. Marzen James P. Crutchfield |
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Affiliation: | 1.W. M. Keck Science Department of Pitzer, Scripps, and Claremont McKenna College, Claremont, CA 91711, USA;2.Complexity Sciences Center and Physics and Astronomy Department, University of California at Davis, One Shields Avenue, Davis, CA 95616, USA |
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Abstract: | 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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Keywords: | Poisson process, renewal process, hidden semi-Markov process, hidden Markov chain, ϵ -machine, Shannon entropy rate, optimal predictor, minimal predictor |
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