Inference, Prediction, & Entropy-Rate Estimation of Continuous-Time, Discrete-Event Processes
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Nov 17, 2022
- Source ID
- 10.3390/e24111675
Entities
People
- James P. Crutchfield
- Sarah E. Marzen
Organizations
- Army Research Office
- Foundational Questions Institute
- Gordon and Betty Moore Foundation
- Templeton World Charity Foundation
- United States Army Research Laboratory
- United States Department of Energy