Methods of likelihood based inference for constructing stochastic climate models

Subject: QA  QC

References
(26)
3.2 Invariant distributions for variables in Eq. (3.31) for = 0:1. . . . .
3.3 Solution of the Galerkin truncation of the Burgers equation for times t = 0; 0:4; 1:5; 20 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.4 Evolution of Fourier amplitudes for k = 1; 5; 10; 20 . . . . . . . . . .
3.5 Example of dynamics of Mean ow Ut from Eq. (3.40) . . . . . . . .
3.6 Comparison of predicted density from equilibrium statistical mechanics and the empirical density for the mean ow. . . . . . . . . . . . .
Y Abe, S Ayik, PG Reinhard, and E Suraud. On stochastic approaches of nuclear dynamics. Physics Reports  Review Section of Physics Letters, 275(23):49{196, OCT 1996.
Y AitSahalia. Maximum likelihood estimation of discretely sampled di usions: A closedform approximation approach. Econometrica, 70(1):223{262, JAN 2002.
Yacine AitSahalia. Closedform likelihood expansions for multivariate di usions. Annals of Statistics, 36(2):906{937, APR 2008.
Arnold. Hasselmann's program revisted: the analysis of stochasticity in deterministic climate models, chapter 2, pages 141{157. Birkhauser, 2001.
Matyas Barczy and Peter Kern. Representations of multidimensional linear process bridges. arXiv:1011.0067v1 [math.PR], 2010.

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