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- W3021559524 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." @default.
- W3021559524 created "2020-05-13" @default.
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- W3021559524 date "2020-05-07" @default.
- W3021559524 modified "2023-10-18" @default.
- W3021559524 title "Inference, Prediction, and Entropy-Rate Estimation of Continuous-time, Discrete-event Processes" @default.
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- W3021559524 doi "https://doi.org/10.48550/arxiv.2005.03750" @default.
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