Rao-Blackwellized Particle MCMC for Parameter Estimation in Spatio-Temporal Gaussian Processes

Proceedings of the 27th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2017, IEEE International Workshop on Machine Learning for Signal Processing

Abstract

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.

DOI

10.1109/MLSP.2017.8168171

Citation

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

Permanent link to this item

https://urn.fi/URN:NBN:fi:aalto-201812105965

Collections

[article-cris] Sähkötekniikan korkeakoulu / ELEC

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