Big data meets big models: Towards exascale Bayesian inverse problems (INVITED)

Abstract eng:
Predictive models of complex systems often contain numerous uncertain parameters. Rapidly expanding volumes of observational data present opportunities to reduce these uncertainties via solution of inverse problems. Bayesian inference provides a systematic framework for inferring model parameters with associated uncertainties from (possibly noisy) data and any prior information. However, solution of the Bayesian inverse problem via conventional Markov chain Monte Carlo methods remains prohibitive for expensive models and high-dimensional parameters. Despite the large size of observational datasets, typically they provide only sparse information on model parameters. Based on this property we design MCMC methods that adapt to the structure of the posterior probability and exploit an effectively-reduced parameter dimension. We apply the methodology to an inverse problem for Antarctic ice sheet flow.

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