@inproceedings{f982a893c63240c4ad589ee03d1e51bf,
title = "Aggregation-based model reduction of a hidden Markov model",
abstract = "This paper is concerned with developing an information-theoretic framework to aggregate the state space of a Hidden Markov Model (HMM) on discrete state and observation spaces. The optimal aggregation is obtained by minimizing the Kullback-Leibler (K-L) divergence rate between joint laws describing the state and observation processes. The solution to this optimization problem is just the optimal aggregated Hidden Markov Model. This optimization problem is solved in two steps: The first step is to formulate the optimal solution for any fixed partition. The second step is to find the optimal partition by using an approximate dynamic programming framework. The algorithm can be implemented using a single sample path of the HMM and is illustrated with the aid of examples.",
author = "Kun Deng and Mehta, {Prashant G.} and Meyn, {Sean P.}",
year = "2010",
doi = "10.1109/CDC.2010.5717118",
language = "English (US)",
isbn = "9781424477456",
series = "Proceedings of the IEEE Conference on Decision and Control",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "6183--6188",
booktitle = "2010 49th IEEE Conference on Decision and Control, CDC 2010",
address = "United States",
note = "49th IEEE Conference on Decision and Control, CDC 2010 ; Conference date: 15-12-2010 Through 17-12-2010",
}