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Some thoughts on Gaussian processes for emulation of deterministic computer models: Michael Stein
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Classroom Contents
Effective and Efficient Gaussian Processes
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- 1 Some thoughts on Gaussian processes for emulation of deterministic computer models: Michael Stein
- 2 Deep Gaussian Processes for Bayesian Inversion: Matt Dunlop, Courant
- 3 High-dimensional hierarchical models for large-scale geophysical applications: Lassi Roininem, LUT
- 4 Known Boundary Emulation of Complex Computer Models: Ian Vernon, Cambridge
- 5 Pragmatically ambitious multiscale global temperature reconstruction: Finn Lindgren, Edinburgh
- 6 Efficient an effective calibration of spatio-temporal models: Dan Williamson & James Salter, Exeter
- 7 Deep and Multi-fidelity learning with Gaussian processes: Andreas Damianou, Amazon
- 8 Integrated emulator for multi-physics systems of computer models: Deyu Ming, UCL
- 9 Sequential Design based on Mutual Information for Computer Experiments: Joakim Beck, KAUST
- 10 GP Emulators applied to UQ workflows in practice: Eric Daub, Turing
