Optimal segmentation using tree models
Knowledge and Information Systems
Unrestricted BIC context tree estimation for not necessarily finite memory processes
ISIT'09 Proceedings of the 2009 IEEE international conference on Symposium on Information Theory - Volume 2
The computational structure of spike trains
Neural Computation
On rate of convergence of statistical estimation of stationary ergodic processes
IEEE Transactions on Information Theory
Learning High-Dimensional Markov Forest Distributions: Analysis of Error Rates
The Journal of Machine Learning Research
Learning markov models for stationary system behaviors
NFM'12 Proceedings of the 4th international conference on NASA Formal Methods
| Hi-index | 754.90 |
The concept of context tree, usually defined for finite memory processes, is extended to arbitrary stationary ergodic processes (with finite alphabet). These context trees are not necessarily complete, and may be of infinite depth. The familiar Bayesian information criterion (BIC) and minimum description length (MDL) principles are shown to provide strongly consistent estimators of the context tree, via optimization of a criterion for hypothetical context trees of finite depth, allowed to grow with the sample size n as o(logn). Algorithms are provided to compute these estimators in O(n) time, and to compute them on-line for all i les n in o(nlogn) time