11 citations · 41 across the 15 of their papers we have counts for
4 papers · 1 filter
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…
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…
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…
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…