Simo Särkkä

Hilbert space methods for reduced-rank Gaussian process regression

2019 · Statistics and Computing

This paper proposes a novel scheme for reduced-rank Gaussian process regression. The method is based on an approximate series expansion of the covariance function in terms of an eigenfunction expansion of the Laplace operator in …

MIXTURE REPRESENTATION OF THE MATÉRN CLASS WITH APPLICATIONS IN STATE SPACE APPROXIMATIONS AND BAYESIAN QUADRATURE

2018

In this paper, the connection between the Matérn kernel and scale mixtures of squared exponential kernels is explored. It is shown that the Matérn kernel can be approximated by a finite scale mixture of squared …

Gaussian Process Latent Force Models for Learning and Stochastic Control of Physical Systems

2019 · Research Explorer (The University of Manchester)

This paper is concerned with learning and stochastic control in physical systems that contain unknown input signals. These unknown signals are modeled as Gaussian processes (GP) with certain parameterized covariance structures. The resulting latent force …

Posterior linearisation smoothing with robust iterations

2021 · arXiv (Cornell University)

This paper considers the problem of iterative Bayesian smoothing in nonlinear state-space models with additive noise using Gaussian approximations. Iterative methods are known to improve smoothed estimates but are not guaranteed to converge, motivating the …

Temporal Gaussian Process Regression in Logarithmic Time

2021 · arXiv (Cornell University)

The aim of this article is to present a novel parallelization method for temporal Gaussian process (GP) regression problems. The method allows for solving GP regression problems in logarithmic O(log N) time, where N is …