Articles | Volume 24, issue 4
Nonlin. Processes Geophys., 24, 701–712, 2017
https://doi.org/10.5194/npg-24-701-2017
Nonlin. Processes Geophys., 24, 701–712, 2017
https://doi.org/10.5194/npg-24-701-2017

Research article 01 Dec 2017

Research article | 01 Dec 2017

The Onsager–Machlup functional for data assimilation

Nozomi Sugiura

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Cited articles

Apte, A., Hairer, M., Stuart, A. M., and Voss, J.: Sampling the posterior: An approach to non-Gaussian data assimilation, Phys. D, 230, 50–64, https://doi.org/10.1016/j.physd.2006.06.009, 2007.
Cotter, S. L., Roberts, G. O., Stuart, A., and White, D.: MCMC methods for functions: modifying old algorithms to make them faster, Stat. Sci., 28, 424–446, 2013.
Daum, F.: Exact finite-dimensional nonlinear filters, IEEE T. Automat. Contr., 31, 616–622, 1986.
Doucet, A., Godsill, S., and Andrieu, C.: On sequential Monte Carlo sampling methods for Bayesian filtering, Stat. Comput., 10, 197–208, 2000.
Dutra, D. A., Teixeira, B. O. S., and Aguirre, L. A.: Maximum a posteriori state path estimation: Discretization limits and their interpretation, Automatica, 50, 1360–1368, 2014.
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The optimisation of simulation paths is sometimes misleading. We can find a path with the highest probability by the method of least squares. However, it is not necessarily the route where the paths are most concentrated. This paper clarifies how we can find the mode of a distribution of paths by optimisation.