most citedState Space Expectation Propagation: Efficient Inference Schemes for Temporal Gaussian Processes

4 citations · 6 across the 3 of their papers we have counts for

collaborators

5 papers

stat.ML20204 cited

State Space Expectation Propagation: Efficient Inference Schemes for Temporal Gaussian Processes

William J. Wilkinson, Paul E. Chang, Michael Riis Andersen +1

We formulate approximate Bayesian inference in non-conjugate temporal and spatio-temporal Gaussian process models as a simple parameter update rule applied during Kalman smoothing.…

stat.ME2020

Leave-One-Out Cross-Validation for Bayesian Model Comparison in Large Data

Måns Magnusson, Michael Riis Andersen, Johan Jonasson +1

Recently, new methods for model assessment, based on subsampling and posterior approximations, have been proposed for scaling leave-one-out cross-validation (LOO) to large datasets…

stat.AP20191 cited

Gaussian process with derivative information for the analysis of the sunlight adverse effects on color of rock art paintings

Gabriel Riutort-Mayol, Michael Riis Andersen, Aki Vehtari +1

Microfading Spectrometry (MFS) is a method for assessing light sensitivity color (spectral) variations of cultural heritage objects. The MFS technique provides measurements of the…

stat.ML2019

Bayesian leave-one-out cross-validation for large data

Måns Magnusson, Michael Riis Andersen, Johan Jonasson +1

Model inference, such as model comparison, model checking, and model selection, is an important part of model development. Leave-one-out cross-validation (LOO) is a general approac…

stat.ML20191 cited

End-to-End Probabilistic Inference for Nonstationary Audio Analysis

William J. Wilkinson, Michael Riis Andersen, Joshua D. Reiss +2

A typical audio signal processing pipeline includes multiple disjoint analysis stages, including calculation of a time-frequency representation followed by spectrogram-based featur…