A nonparametric ensemble transform method for bayesian inference

SIAM Journal on Scientific Computing

Volumn 35, Issue 4, 2013, Pages

  Reich, Sebastian ^a,b  

^a UNIVERSITY OF POTSDAM   (Germany)

^b UNIVERSITY OF READING   (United Kingdom)

Author keywords

Bayesian inference; Linear programming; Monte Carlo method; Resampling; Sequential data assimilation

Indexed keywords

BAYESIAN NETWORKS; IMPORTANCE SAMPLING; INFERENCE ENGINES; KALMAN FILTERS; LINEAR PROGRAMMING; LINEAR TRANSFORMATIONS; MATHEMATICAL TRANSFORMATIONS;

BAYESIAN INFERENCE; DATA ASSIMILATION ALGORITHMS; DISCRETE RANDOM VARIABLES; OPTIMAL TRANSPORTATIONS; POSTERIOR DISTRIBUTIONS; RESAMPLING; SEQUENTIAL DATA; SEQUENTIAL MONTE CARLO METHODS;

MONTE CARLO METHODS;

EID: 84886860357     PISSN: 10648275     EISSN: 10957200     Source Type: Journal    
DOI: 10.1137/130907367     Document Type: Article

References (19)

1. J. Anderson, A non-Gaussian ensemble filter update for data assimilation, Monthly Weather Rev., 138 (2010), pp. 4186-4198.
2. M. Arulampalam, S. Maskell, N. Gordon, and T. Clapp, A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking, IEEE Trans. Signal Process., 50 (2002), pp. 174-188. (Pubitemid 34291500)
3. A. Bain and D. Crisan, Fundamentals of Stochastic Filtering, Stoch. Model. Appl. Probab., Springer-Verlag, New York, 2008.
4. D. Bertsekas and D. Castanon, The auction algorithm for the transportation problem, Ann. Oper. Res., 20 (1989), pp. 67-96.
5. C. Cotter and S. Reich, Ensemble filter techniques for intermittent data assimilation, Large Scale Inverse Problems. Computational Methods and Applications in the Earth Sciences, Radon Ser. Comput. Appl. Math., Walter de Gruyter, Berlin, 13 (2013), pp. 91-134.
6. A. Doucet, N. de Freitas, and N. Gordon, eds., Sequential Monte Carlo Methods in Practice, Springer-Verlag, New York, 2001.
7. G. Evensen, Data Assimilation. The Ensemble Kalman Filter, Springer-Verlag, New York, 2006.
8. A. Jazwinski, Stochastic Processes and Filtering Theory, Academic Press, New York, 1970.
9. H. Künsch, Recursive Monte Carlo Filter: Algorithms and Theoretical Analysis, Ann. Statist., 33 (2005), pp. 1983-2021. (Pubitemid 43059668)
10. J. Lei and P. Bickel, A moment matching ensemble filter for nonlinear and non-Gaussian data assimilation, Monthly Weather Rev., 139 (2011), pp. 3964-3973.
11. J. Liu, Monte Carlo Strategies in Scientific Computing, Springer-Verlag, New York, 2001.
12. E. Lorenz, Deterministic non-periodic flows, J. Atmospheric Sci., 20 (1963), pp. 130-141.
13. R. McCann, Existence and uniqueness of monotone measure-preserving maps, Duke Math. J., 80 (1995), pp. 309-323.
14. T. E. Moselhy and Y. Marzouk, Bayesian inference with optimal maps, J. Comput. Phys., 231 (2012), pp. 7815-7850.
15. S. Reich, A dynamical systems framework for intermittent data assimilation, BIT, 51 (2011), pp. 235-249.
16. S. Reich, A Gaussian mixture ensemble transform filter, Quart. J. Roy. Meterolog. Soc., 138 (2012), pp. 222-233.
17. G. Strang, Introduction to Applied Mathematics, 2nd ed., Wellesley Cambridge Press, Wellesley, MA, 1986.
18. C. Villani, Topics in Optimal Transportation, AMS, Providence, RI, 2003.
19. C. Villani, Optimal Transportation: Old and New, Springer-Verlag, Heidelberg, 2009.