Abstract
We propose a novel stochastic approach to reconstruct the unknown input of a partly known dynamical system from noisy output data. We assume that the unknown function belongs to a Reproducing Kernel Hilbert Space (RKHS). We then design an algorithm based on the Markov chain Monte Carlo (MCMC) framework which is able to recover the minimum variance estimate of the input given the output data.
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Pillonetto, G., Bell, B.M. (2005). Bayesian Deconvolution of Functions in RKHS Using MCMC Techniques. In: Cagnol, J., Zolésio, JP. (eds) System Modeling and Optimization. CSMO 2003. IFIP International Federation for Information Processing, vol 166. Springer, Boston, MA. https://doi.org/10.1007/0-387-23467-5_18
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DOI: https://doi.org/10.1007/0-387-23467-5_18
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4020-7760-9
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