Gaussian regression combined with stochastic simulation schemes has proved to be an effective tool in hybrid systems estimation. In particular, recent works have shown that this approach can face effectively both the classification and estimation tasks jointly involved in this problem. In this paper, the combinatorial aspect arising in the choice between linear or nonlinear submodels is overcome with a new Gibbs sampling scheme. Numerical examples concerning the case of discontinuous (static) function estimation are provided to test this new approach.

A New Model Selection Approach to Hybrid Kernel-Based Estimation

Scampicchio A.
;
Pillonetto G.
2019

Abstract

Gaussian regression combined with stochastic simulation schemes has proved to be an effective tool in hybrid systems estimation. In particular, recent works have shown that this approach can face effectively both the classification and estimation tasks jointly involved in this problem. In this paper, the combinatorial aspect arising in the choice between linear or nonlinear submodels is overcome with a new Gibbs sampling scheme. Numerical examples concerning the case of discontinuous (static) function estimation are provided to test this new approach.
2019
Proceedings of the IEEE Conference on Decision and Control
978-1-5386-1395-5
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/3389512
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