Inference, Prediction, and Entropy-Rate Estimation of Continuous-time, Discrete-event Processes
This work addresses a gap in modeling continuous-time processes, which is incremental as it extends existing discrete-time methods to a broader class of applications.
The authors tackled the problem of modeling continuous-time discrete-event processes by introducing new methods for inference, prediction, and entropy-rate estimation, achieving competitive performance with state-of-the-art approaches on complex synthetic data.
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.