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- W2897802292 abstract "Abstract The identification of static parameters in jump Markov nonlinear models (JMNMs) poses a key challenge in explaining nonlinear and abruptly changing behavior of dynamical systems. This paper introduces a stochastic approximation expectation maximization algorithm to facilitate offline maximum likelihood parameter estimation in JMNMs. The method relies on the construction of a particle Gibbs kernel that takes advantage of the inherent structure of the model to increase the efficiency through Rao-Blackwellization. Numerical examples illustrate that the proposed solution outperforms related approaches." @default.
- W2897802292 created "2018-10-26" @default.
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- W2897802292 date "2018-01-01" @default.
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- W2897802292 title "A Particle Stochastic Approximation EM Algorithm to Identify Jump Markov Nonlinear Models" @default.
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- W2897802292 doi "https://doi.org/10.1016/j.ifacol.2018.09.205" @default.
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