Rao-Blackwellized Particle MCMC for Parameter Estimation in Spatio-Temporal Gaussian Processes
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A4 Artikkeli konferenssijulkaisussa
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en
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6
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Proceedings of the 27th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2017, IEEE International Workshop on Machine Learning for Signal Processing
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In this paper, we consider parameter estimation in latent, spatiotemporal Gaussian processes using particle Markov chain Monte Carlo methods. In particular, we use spectral decomposition of the covariance function to obtain a high-dimensional state-space representation of the Gaussian processes, which is assumed to be observed through a nonlinear non-Gaussian likelihood. We develop a Rao-Blackwellized particle Gibbs sampler to sample the state trajectory and show how to sample the hyperparameters and possible parameters in the likelihood. The proposed method is evaluated on a spatio-temporal population model and the predictive performance is evaluated using leave-one-out cross-validation.Description
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Hostettler, R, Särkkä, S & Godsill, S J 2017, Rao-Blackwellized Particle MCMC for Parameter Estimation in Spatio-Temporal Gaussian Processes. in Proceedings of the 27th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2017 . IEEE International Workshop on Machine Learning for Signal Processing, IEEE, IEEE International Workshop on Machine Learning for Signal Processing, Tokyo, Japan, 25/09/2017. https://doi.org/10.1109/MLSP.2017.8168171