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Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC
Department of Engineering, University of Cambridge, UK.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Deptartment of Information Technology, Uppsala University, Sweden.
Department of Engineering, University of Cambridge, UK.
2013 (English)In: Advances in Neural Information Processing Systems 26 / [ed] C.J.C. Burges, L. Bottou, M. Welling, Z. Ghahramani and K.Q. Weinberger, 2013Conference paper, Published paper (Refereed)
Abstract [en]

State-space models are successfully used in many areas of science, engineering and economics to model time series and dynamical systems. We present a fully Bayesian approach to inference and learning in nonlinear nonparametric state-space models. We place a Gaussian process prior over the transition dynamics, resulting in a flexible model able to capture complex dynamical phenomena. However, to enable efficient inference, we marginalize over the dynamics of the model and instead infer directly the joint smoothing distribution through the use of specially tailored Particle Markov Chain Monte Carlo samplers. Once an approximation of the smoothing distribution is computed, the state transition predictive distribution can be formulated analytically. We make use of sparse Gaussian process models to greatly reduce the computational complexity of the approach.

Place, publisher, year, edition, pages
2013.
National Category
Control Engineering Signal Processing
Identifiers
URN: urn:nbn:se:liu:diva-103917OAI: oai:DiVA.org:liu-103917DiVA: diva2:692906
Conference
Neural Information Processing Systems (NIPS)
Projects
CNDMCADICS
Available from: 2014-02-03 Created: 2014-02-03 Last updated: 2014-02-03

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Lindsten, Fredrik

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