Optimal quantisation of probability measures using maximum mean discrepancy

no code implementations • 14 Oct 2020 • Onur Teymur, Jackson Gorham, Marina Riabiz, Chris. J. Oates

Several researchers have proposed minimisation of maximum mean discrepancy (MMD) as a method to quantise probability measures, i. e., to approximate a target distribution by a representative point set.

Optimal Thinning of MCMC Output

3 code implementations • pproximateinference AABI Symposium 2021 • Marina Riabiz, Wilson Chen, Jon Cockayne, Pawel Swietach, Steven A. Niederer, Lester Mackey, Chris. J. Oates

The use of heuristics to assess the convergence and compress the output of Markov chain Monte Carlo can be sub-optimal in terms of the empirical approximations that are produced.

Considering discrepancy when calibrating a mechanistic electrophysiology model

1 code implementation • 13 Jan 2020 • Chon Lok Lei, Sanmitra Ghosh, Dominic G. Whittaker, Yasser Aboelkassem, Kylie A. Beattie, Chris D. Cantwell, Tammo Delhaas, Charles Houston, Gustavo Montes Novaes, Alexander V. Panfilov, Pras Pathmanathan, Marina Riabiz, Rodrigo Weber dos Santos, John Walmsley, Keith Worden, Gary R. Mirams, Richard D. Wilkinson

Uncertainty quantification (UQ) is a vital step in using mathematical models and simulations to take decisions.

Gaussian Processes Uncertainty Quantification