Excursions in the Bayesian treatment of model error.

Advances in observational and computational assets have led to revolutions in the range and quality of results in many science and engineering settings. However, those advances have led to needs for new research in treating model errors and assessing their impacts. We consider two settings. The first involves physically-based statistical models that are sufficiently manageable to allow incorporation of a stochastic "model error process". In the second case we consider large-scale models in which incorporation of a model error process and updating its distribution is impractical. Our suggestion is to treat dimension-reduced model output as if it is observational data, with a data model that incorporates a bias component to represent the impacts of model error. We believe that our suggestions are valuable quantitative, yet relatively simple, ways to extract useful information from models while including adjustment for model error. These ideas are illustrated and assessed using an application inspired by a classical oceanographic problem.

Applying diffusion-based Markov chain Monte Carlo.

We examine the performance of a strategy for Markov chain Monte Carlo (MCMC) developed by simulating a discrete approximation to a stochastic differential equation (SDE). We refer to the approach as diffusion MCMC. A variety of motivations for the approach are reviewed in the context of Bayesian analysis. In particular, implementation of diffusion MCMC is very simple to set-up, even in the presence of nonlinear models and non-conjugate priors. Also, it requires comparatively little problem-specific tuning. We implement the algorithm and assess its performance for both a test case and a glaciological application. Our results demonstrate that in some settings, diffusion MCMC is a faster alternative to a general Metropolis-Hastings algorithm.