11 citations · 41 across the 15 of their papers we have counts for
20 papers
Theoretical and Practical Advances in Hierarchical Bayesian Deep Learning Methodology
Neil K. Chada, Simon Cotter, Kody J. H. Law +1
Reliable uncertainty quantification remains an important challenge in AI. Hierarchical Bayesian methods provide a principled framework for this by representing unknown quantities t…
Sampling as Bandits: Evaluation-Efficient Design for Black-Box Densities
Takuo Matsubara, Andrew Duncan, Simon Cotter +1
We propose bandit importance sampling (BIS), a powerful importance sampling framework tailored for settings in which evaluating the target density is computationally expensive. BIS…
Exploring natural variation in tendon constitutive parameters via Bayesian data selection and mixed effects models
James Casey, Jessica Forsyth, Timothy Waite +2
Combining microstructural mechanical models with experimental data enhances our understanding of the mechanics of soft tissue, such as tendons. In previous work, a Bayesian framewo…
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…
Batch Bayesian Optimization via Particle Gradient Flows
Enrico Crovini, Simon L. Cotter, Konstantinos Zygalakis +1
Bayesian Optimisation (BO) methods seek to find global optima of objective functions which are only available as a black-box or are expensive to evaluate. Such methods construct a…
Hierarchical Bayesian data selection
Simon L. Cotter
There are many issues that can cause problems when attempting to infer model parameters from data. Data and models are both imperfect, and as such there are multiple scenarios in w…
Unlabelled landmark matching via Bayesian data selection, and application to cell matching across imaging modalities
Jessica E. Forsyth, Ali H. Al-Anbaki, Berenika Plusa +1
We consider the problem of landmark matching between two unlabelled point sets, in particular where the number of points in each cloud may differ, and where points in each cloud ma…
Bayesian inference on a microstructural, hyperelastic model of tendon deformation
James Haughton, Simon L. Cotter, William J. Parnell +1
Microstructural models of soft tissue deformation are important in applications including artificial tissue design and surgical planning. The basis of these models, and their advan…
Efficient forecasting and uncertainty quantification for large scale account level Monte Carlo models of debt recovery
Sam Baynes, Simon Cotter, Paul Russell +2
We consider the problem of forecasting debt recovery from large portfolios of non-performing unsecured consumer loans under management. The state of the art in industry is to use s…
NuZZ: numerical Zig-Zag sampling for general models
Filippo Pagani, Augustin Chevallier, Sam Power +2
Markov chain Monte Carlo (MCMC) is a key algorithm in computational statistics, and as datasets grow larger and models grow more complex, many popular MCMC algorithms become too co…
Transport map accelerated adaptive importance sampling, and application to inverse problems arising from multiscale stochastic reaction networks
Simon L. Cotter, Ioannis G. Kevrekidis, Paul Russell
In many applications, Bayesian inverse problems can give rise to probability distributions which contain complexities due to the Hessian varying greatly across parameter space. Thi…
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…
Product-form stationary distributions for deficiency zero networks with non-mass action kinetics
David F. Anderson, Simon L. Cotter
In many applications, for example when computing statistics of fast subsystems in a multiscale setting, we wish to find the stationary distributions of systems of continuous time M…
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…
Bayesian data assimilation in shape registration
C. J. Cotter, S. L. Cotter, F. -X. Vialard
In this paper we apply a Bayesian framework to the problem of geodesic curve matching. Given a template curve, the geodesic equations provide a mapping from initial conditions for…
MCMC Methods for Functions: Modifying Old Algorithms to Make Them Faster
S. L. Cotter, G. O. Roberts, A. M. Stuart +1
Many problems arising in applications result in the need to probe a probability distribution for functions. Examples include Bayesian nonparametric statistics and conditioned diffu…
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…