Practical Hilbert space approximate Bayesian Gaussian processes for probabilistic programming
DOI10.48550/arXiv.2004.11408zbMath1502.62024arXiv2004.11408OpenAlexW4311305286MaRDI QIDQ91882
Paul-Christian Bürkner, Aki Vehtari, Michael R. Andersen, Arno Solin, Gabriel Riutort-Mayol, Arno Solin, Paul-Christian Bürkner, Aki Vehtari, Michael R. Andersen, Gabriel Riutort-Mayol
Publication date: 23 April 2020
Published in: Statistics and Computing (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2004.11408
Gaussian processBayesian statisticsHilbert space methodssparse Gaussian processlow-rank Gaussian process
Computational methods for problems pertaining to statistics (62-08) Gaussian processes (60G15) Bayesian inference (62F15)
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Cites Work
- Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC
- Hilbert space methods for reduced-rank Gaussian process regression
- A survey of Bayesian predictive methods for model assessment, selection and comparison
- Probabilistic integration: a role in statistical computation?
- A framework to understand the asymptotic properties of kriging and splines
- Statistical Analysis of Spatial and Spatio-Temporal Point Patterns
- Handbook of Markov Chain Monte Carlo
- Regression and Other Stories
- Probabilistic numerics and uncertainty in computations
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