This chapter reviews the recent advances in Bayesian filtering approaches, with the focus on those suitable for data assimilation in high-dimensional systems. We discuss the similarities and differences of these filtering approaches, and compare their performance in an application to the history matching problem in a synthetic, twodimensional, oil-water reservoir model.
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