153 citations · 179 across the 6 of their papers we have counts for
7 papers
On Bayesian Generalized Additive Models
Antti Solonen, Stratos Staboulis
Generalized additive models (GAMs) provide a way to blend parametric and non-parametric (function approximation) techniques together, making them flexible tools suitable for many m…
Hierarchical Bayesian propulsion power models for marine vessels
Antti Solonen, Ramona Maraia, Sebastian Springer +5
Assessing the magnitude of fuel consumption of marine traffic is a challenging task. The consumption can be reduced by the ways the vessels are operated, to achieve both improved c…
Undersampled dynamic X-ray tomography with dimension reduction Kalman filter
Janne Hakkarainen, Zenith Purisha, Antti Solonen +1
In this paper, we consider prior-based dimension reduction Kalman filter for undersampled dynamic X-ray tomography. With this method, the X-ray reconstructions are parameterized by…
Bayesian inverse problems with priors: a Randomize-then-Optimize approach
Zheng Wang, Johnathan M. Bardsley, Antti Solonen +2
Prior distributions for Bayesian inference that rely on the -norm of the parameters are of considerable interest, in part because they promote parameter fields with less regul…
On dimension reduction in Gaussian filters
Antti Solonen, Tiangang Cui, Janne Hakkarainen +1
A priori dimension reduction is a widely adopted technique for reducing the computational complexity of stationary inverse problems. In this setting, the solution of an inverse pro…
Optimal low-rank approximations of Bayesian linear inverse problems
Alessio Spantini, Antti Solonen, Tiangang Cui +3
In the Bayesian approach to inverse problems, data are often informative, relative to the prior, only on a low-dimensional subspace of the parameter space. Significant computationa…
Likelihood-informed dimension reduction for nonlinear inverse problems
Tiangang Cui, James Martin, Youssef M. Marzouk +2
The intrinsic dimensionality of an inverse problem is affected by prior information, the accuracy and number of observations, and the smoothing properties of the forward operator.…