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原文連結
論文資訊
- 類型：已發表論文
- 日期：2020
摘要
Inferring models, predicting the future, and estimating the 熵 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 貝氏 structural inference that takes advantage of 神經 網絡's universal approximation power. Based on experiments with complex synthetic data, the methods are competitive with the state-of-the-art for prediction and 熵-rate estimation.
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