Articles | Volume 28, issue 1
Research article
15 Jan 2021
Research article |  | 15 Jan 2021

Fast hybrid tempered ensemble transform filter formulation for Bayesian elliptical problems via Sinkhorn approximation

Sangeetika Ruchi, Svetlana Dubinkina, and Jana de Wiljes

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Subject: Predictability, probabilistic forecasts, data assimilation, inverse problems | Topic: Climate, atmosphere, ocean, hydrology, cryosphere, biosphere | Techniques: Simulation
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Cited articles

Acevedo, W., de Wiljes, J., and Reich, S.: Second-order Accurate Ensemble Transform Particle Filters, SIAM J. Sci. Comput., 39, A1834–A1850, 2017. a, b
Agapiou, S., Papaspiliopoulos, O., Sanz-Alonso, D., and Stuart, A. M.: Importance sampling: computational complexity and intrinsic dimension, Stat. Sci., 32, 405–431,, 2017. a
Bardsley, J., Solonen, A., Haario, H., and Laine, M.: Randomize-then-optimize: A method for sampling from posterior distributions in nonlinear inverse problems, SIAM J. Sci. Comput., 36, A1895–A1910, 2014. a, b
Beskos, A., Crisan, D., and Jasra, A.: On the stability of sequential Monte Carlo methods in high dimensions, Ann. Appl. Probab., 24, 1396–1445,, 2014. a
Beskos, A., Jasra, A., Muzaffer, E. A., and Stuart, A. M.: Sequential Monte Carlo methods for Bayesian elliptic inverse problems, Stat. Comput., 25, 727–737,, 2015. a
Short summary
To infer information of an unknown quantity that helps to understand an associated system better and to predict future outcomes, observations and a physical model that connects the data points to the unknown parameter are typically used as information sources. Yet this problem is often very challenging due to the fact that the unknown is generally high dimensional, the data are sparse and the model can be non-linear. We propose a novel approach to address these challenges.