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- W2963954278 abstract "Gaussian process state-space models (GP-SSMs) are a very flexible family of models of nonlinear dynamical systems. They comprise a Bayesian nonparametric representation of the dynamics of the system and additional (hyper-)parameters governing the properties of this nonparametric representation. The Bayesian formalism enables systematic reasoning about the uncertainty in the system dynamics. We present an approach to maximum likelihood identification of the parameters in GP-SSMs, while retaining the full nonparametric description of the dynamics. The method is based on a stochastic approximation version of the EM algorithm that employs recent developments in particle Markov chain Monte Carlo for efficient identification." @default.
- W2963954278 created "2019-07-30" @default.
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- W2963954278 date "2014-01-01" @default.
- W2963954278 modified "2023-10-16" @default.
- W2963954278 title "Identification of Gaussian Process State-Space Models with Particle Stochastic Approximation EM" @default.
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- W2963954278 doi "https://doi.org/10.3182/20140824-6-za-1003.01843" @default.
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