11 citations · 41 across the 15 of their papers we have counts for
7 papers · 1 filter
A randomized multi-index sequential Monte Carlo method
Xinzhu Liang, Shangda Yang, Simon L. Cotter +1
We consider the problem of estimating expectations with respect to a target distribution with an unknown normalizing constant, and where even the unnormalized target needs to be ap…
Surrogate Accelerated Bayesian Inversion for the Determination of the Thermal Diffusivity of a Material
James Rynn, Simon Cotter, Catherine E Powell +1
Determination of the thermal properties of a material is an important task in many scientific and engineering applications. How a material behaves when subjected to high or fluctua…
Ensemble Transport Adaptive Importance Sampling
Colin Cotter, Simon Cotter, Paul Russell
Markov chain Monte Carlo methods are a powerful and commonly used family of numerical methods for sampling from complex probability distributions. As applications of these methods…
Constrained Approximation of Effective Generators for Multiscale Stochastic Reaction Networks and Application to Conditioned Path Sampling
Simon Cotter
Efficient analysis and simulation of multiscale stochastic systems of chemical kinetics is an ongoing area for research, and is the source of many theoretical and computational cha…
Error Analysis of Diffusion Approximation Methods for Multiscale Systems in Reaction Kinetics
Simon Cotter, Radek Erban
Several different methods exist for efficient approximation of paths in multiscale stochastic chemical systems. Another approach is to use bursts of stochastic simulation to estima…
Adaptive Finite Element Method Assisted by Stochastic Simulation of Chemical Systems
Simon L. Cotter, Tomas Vejchodsky, Radek Erban
Stochastic models of chemical systems are often analysed by solving the corresponding Fokker-Planck equation which is a drift-diffusion partial differential equation for the probab…
Approximation of Bayesian Inverse Problems for PDEs
S. L. Cotter, M. Dashti, A. M. Stuart
Inverse problems are often ill-posed, with solutions that depend sensitively on data. In any numerical approach to the solution of such problems, regularization of some form is nee…