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• W2044694249 abstract "This paper is concerned with a recursive learning algorithm for model reduction of Hidden Markov Models (HMMs) with finite state space and finite observation space. The state space is aggregated/partitioned to reduce the complexity of the HMM. The optimal aggregation is obtained by minimizing the Kullback-Leibler divergence rate between the laws of the observation process. The optimal aggregated HMM is given as a function of the partition function of the state space. The optimal partition is obtained by using a recursive stochastic approximation learning algorithm, which can be implemented through a single sample path of the HMM. Convergence of the algorithm is established using ergodicity of the filtering process and standard stochastic approximation arguments." @default.
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• W2044694249 date "2011-12-01" @default.
• W2044694249 modified "2023-09-28" @default.
• W2044694249 title "A recursive learning algorithm for model reduction of Hidden Markov Models" @default.
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• W2044694249 doi "https://doi.org/10.1109/cdc.2011.6160826" @default.
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