5 papers
Post-Processing of MCMC
Leah F. South, Marina Riabiz, Onur Teymur +1
Markov chain Monte Carlo (MCMC) is the engine of modern Bayesian statistics, being used to approximate the posterior and derived quantities of interest. Despite this, the issue of…
Optimal quantisation of probability measures using maximum mean discrepancy
Onur Teymur, Jackson Gorham, Marina Riabiz +1
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 repre…
The Lévy State Space Model
Simon Godsill, Marina Riabiz, Ioannis Kontoyiannis
In this paper we introduce a new class of state space models based on shot-noise simulation representations of non-Gaussian Lévy-driven linear systems, represented as stochastic di…
Considering discrepancy when calibrating a mechanistic electrophysiology model
Chon Lok Lei, Sanmitra Ghosh, Dominic G. Whittaker +14
Uncertainty quantification (UQ) is a vital step in using mathematical models and simulations to take decisions. The field of cardiac simulation has begun to explore and adopt UQ me…
Nonasymptotic Gaussian Approximation for Inference with Stable Noise
Marina Riabiz, Tohid Ardeshiri, Ioannis Kontoyiannis +1
The results of a series of theoretical studies are reported, examining the convergence rate for different approximate representations of -stable distributions. Although they pla…