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Inferring models, predicting the future, and estimating the entropy rate ofdiscrete-time, discrete-event processes is well-worn ground. However, a muchbroader class of discrete-event processes operates in continuous-time. Here, weprovide new methods for inferring, predicting, and estimating them. The methodsrely on an extension of Bayesian structural inference that takes advantage ofneural network's universal approximation power. Based on experiments withcomplex synthetic data, the methods are competitive with the state-of-the-artfor prediction and entropy-rate estimation.

Inference, Prediction, and Entropy-Rate Estimation of Continuous-time, Discrete-event Processes