2020/05/07 by Sarah Marzen, James P. Crutchfield, Marzen, S. E. +1
Computer Science · Neuroscience · #Chaotic Dynamics (nlin.CD) #FOS: Computer and information sciences #FOS: Physical sciences #Gaussian Processes and Bayesian Inference #Information Theory (cs.IT) #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Neural Networks and Applications #Neural dynamics and brain function #Statistical Mechanics (cond-mat.stat-mech)
paper · pdf · doi:10.48550/arxiv.2005.03750
openalex publication_date 2020/05/07 · openalex created_date 2020/05/13 · openalex updated_date 2026/08/01
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.