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[1] A. Apte, C. K. R. T. Jones, A. M. Stuart, and J. Voss, Data assimilation: Mathematical and statistical perspectives, Internat. J. Numer. Methods Fluids, 56 (2008), pp. 10331046.
[2] S. R. Arridge, J. P. Kaipio, V. Kolehmainen, M. Schweiger, E. Somersalo, and T. Tarvainen, Approximation errors and model reduction with an application to optical diffusion tomography, Inverse Problems, 22 (2006), pp. 175195.
[3] V. I. Bogachev, Gaussian Measures, American Mathematical Society, Providence, RI, 1998.
[4] D. Calvetti and E. Somersalo, Introduction to bayesian scientific computing, Surveys and Tutorials in the Applied Mathematical Sciences 2, Springer, New York, 2007.
[5] S. L. Cotter, M. Dashti, J. C. Robinson, and A. M. Stuart, Bayesian inverse problems for functions and applications to fluid mechanics, Inverse Problems, 25 (2010), p. 115008.
[6] S. L. Cotter, M. Dashti, J. C. Robinson, and A. M. Stuart, MCMC Methods on Function Space and Applications to Fluid Mechanics, in preparation, 2010.
[7] M. Dashti and J. C. Robinson, A simple proof of uniqueness of the particle trajectories for solutions of the NavierStokes equations, Nonlinearity, 22 (2009), pp. 735746.
[8] H. K. Engl, M. Hanke, and A. Neubauer, Regularization of inverse problems, Kluwer, Dordrecht, the Netherlands, 1996.
[9] J. N. Franklin, Wellposed stochastic extensions of ill posed linear problems, J. Math. Anal. Appl., 31 (1970), pp. 682716.
[10] A. Hofinger and H. K. Pikkarainen, Convergence rates for the Bayesian approach to linear inverse problems, Inverse Problems, 23 (2007), pp. 24692484.
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