Mixed LICORS: A Nonparametric Algorithm for Predictive State Reconstruction
Abstract
We introduce mixed LICORS, an algorithm for learning nonlinear, high-dimensional dynamics from spatio-temporal data, suitable for both prediction and simulation. Mixed LICORS extends the recent LICORS algorithm (Goerg and Shalizi, 2012) from hard clustering of predictive distributions to a non-parametric, EM-like soft clustering. This retains the asymptotic predictive optimality of LICORS, but, as we show in simulations, greatly improves out-of-sample forecasts with limited data. The new method is implemented in the publicly-available R package LICORS.
Cite
Text
Goerg and Shalizi. "Mixed LICORS: A Nonparametric Algorithm for Predictive State Reconstruction." International Conference on Artificial Intelligence and Statistics, 2013.Markdown
[Goerg and Shalizi. "Mixed LICORS: A Nonparametric Algorithm for Predictive State Reconstruction." International Conference on Artificial Intelligence and Statistics, 2013.](https://mlanthology.org/aistats/2013/goerg2013aistats-mixed/)BibTeX
@inproceedings{goerg2013aistats-mixed,
title = {{Mixed LICORS: A Nonparametric Algorithm for Predictive State Reconstruction}},
author = {Goerg, Georg M. and Shalizi, Cosma Rohilla},
booktitle = {International Conference on Artificial Intelligence and Statistics},
year = {2013},
pages = {289-297},
url = {https://mlanthology.org/aistats/2013/goerg2013aistats-mixed/}
}