137 citations · 550 across the 55 of their papers we have counts for
4 papers · 1 filter
Practical Hilbert space approximate Bayesian Gaussian processes for probabilistic programming
Gabriel Riutort-Mayol, Paul-Christian Bürkner, Michael R. Andersen +2
Gaussian processes are powerful non-parametric probabilistic models for stochastic functions. However, the direct implementation entails a complexity that is computationally intrac…
Nonlinear State Space Model Identification Using a Regularized Basis Function Expansion
Andreas Svensson, Thomas B. Schön, Arno Solin +1
This paper is concerned with black-box identification of nonlinear state space models. By using a basis function expansion within the state space model, we obtain a flexible struct…
Computationally Efficient Bayesian Learning of Gaussian Process State Space Models
Andreas Svensson, Arno Solin, Simo Särkkä +1
Gaussian processes allow for flexible specification of prior assumptions of unknown dynamics in state space models. We present a procedure for efficient Bayesian learning in Gaussi…
Infinite-dimensional Bayesian filtering for detection of quasi-periodic phenomena in spatio-temporal data
Arno Solin, Simo Särkkä
This paper introduces a spatio-temporal resonator model and an inference method for detection and estimation of nearly periodic temporal phenomena in spatio-temporal data. The mode…