Bayesian ODE solvers: the maximum a posteriori estimate
There is a growing interest in probabilistic numerical solutions to ordinary differential
equations. In this paper, the maximum a posteriori estimate is studied under the class of ν ν …
equations. In this paper, the maximum a posteriori estimate is studied under the class of ν ν …
[HTML][HTML] Wind load estimation and virtual sensing in long-span suspension bridges using physics-informed Gaussian process latent force models
Wind loading is an essential aspect in the design and assessment of long-span bridges, but
it is often not well-known and cannot be measured directly. Most structural health monitoring …
it is often not well-known and cannot be measured directly. Most structural health monitoring …
Probabilistic linear solvers for machine learning
Linear systems are the bedrock of virtually all numerical computation. Machine learning
poses specific challenges for the solution of such systems due to their scale, characteristic …
poses specific challenges for the solution of such systems due to their scale, characteristic …
A general linear-time inference method for Gaussian Processes on one dimension
Gaussian Processes (GPs) provide powerful probabilistic frameworks for interpolation,
forecasting, and smoothing, but have been hampered by computational scaling issues. Here …
forecasting, and smoothing, but have been hampered by computational scaling issues. Here …
Linear-time inference for Gaussian Processes on one dimension
Gaussian Processes (GPs) provide powerful probabilistic frameworks for interpolation,
forecasting, and smoothing, but have been hampered by computational scaling issues. Here …
forecasting, and smoothing, but have been hampered by computational scaling issues. Here …
Linear cost and exponentially convergent approximation of Gaussian Mat\'ern processes
The computational cost for inference and prediction of statistical models based on Gaussian
processes with Mat\'ern covariance functions scales cubicly with the number of observations …
processes with Mat\'ern covariance functions scales cubicly with the number of observations …
Sparse approximations of fractional Matérn fields
We consider fast lattice approximation methods for a solution of a certain stochastic non‐
local pseudodifferential operator equation. This equation defines a Matérn class random …
local pseudodifferential operator equation. This equation defines a Matérn class random …
Temporal gaussian process regression in logarithmic time
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 …
process (GP) regression problems. The method allows for solving GP regression problems …
Mixture representation of the Matérn class with applications in state space approximations and Bayesian quadrature
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 …
exponential kernels is explored. It is shown that the Matérn kernel can be approximated by a …