Inference, Prediction, and Entropy-Rate Estimation of Continuous-time, Discrete-event Processes
Statistical Mechanics
2020-05-11 v1 Information Theory
Machine Learning
math.IT
Chaotic Dynamics
Machine Learning
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.
Cite
@article{arxiv.2005.03750,
title = {Inference, Prediction, and Entropy-Rate Estimation of Continuous-time, Discrete-event Processes},
author = {S. E. Marzen and J. P. Crutchfield},
journal= {arXiv preprint arXiv:2005.03750},
year = {2020}
}
Comments
11 pages, 5 figures; http://csc.ucdavis.edu/~cmg/compmech/pubs/ctbsi.htm