Dan Crisan

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Darryl D HolmProfessor of Mathematics, Imperial College LondonVerified email at ic.ac.uk
Ajay JasraProfessor of Data Science, The Chinese University of Hong Kong, ShenzhenVerified email at cuhk.edu.cn
Joaquin MiguezDepartment of Signal Theory & Communications, Universidad Carlos III de MadridVerified email at tsc.uc3m.es
Alexandros BeskosProfessor in Statistics, UCLVerified email at ucl.ac.uk
Arnaud DoucetGoogle DeepMindVerified email at google.com
Igor ShevchenkoImperial College London, Department of MathematicsVerified email at imperial.ac.uk
Jean-François ChassagneuxProfessor of Applied Mathematics, Université Paris Cité, LPSMVerified email at lpsm.paris
Colin CotterProfessor of Computational Mathematics, Imperial College LondonVerified email at imperial.ac.uk
François DelarueUniversité Côte d'AzurVerified email at unice.fr
Pierre Del MoralResearch Director, INRIA Bordeaux - Sud OuestVerified email at inria.fr
Oana LangResearch Associate, Imperial College LondonVerified email at imperial.ac.uk
Jie XiongSouthern University of Science and TechnologyVerified email at sustech.edu.cn
Michela OttobreHeriot WattVerified email at hw.ac.uk
Wei PanProfessor of Sustainable Construction, Engineering and Management, The University of Hong KongVerified email at hku.hk
Nikolas KantasDept. of Mathematics, Imperial College LondonVerified email at imperial.ac.uk
Salvador Ortiz-LatorreAssociate Professor, University of OsloVerified email at math.uio.no
Peter K. FrizProfessor of Mathematics, TU and WIAS BerlinVerified email at math.tu-berlin.de
Thomas G. KurtzProfessor Emeritus, Mathematics and Statistics, University of Wisconsin-MadisoVerified email at math.wisc.edu
TOUZI NizarEcole PolytechniqueVerified email at polytechnique.edu
Daniel PaulinLecturer of Statistics and Data Science, University of EdinburghVerified email at ed.ac.uk

Dan Crisan

Professor of Mathematics, Imperial College London

Verified email at imperial.ac.uk - Homepage

Stochastic Analysis Data Assimilation SPDEs Particle Approximations Data Assimilation


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A survey of convergence results on particle filtering methods for practitioners D Crisan, A Doucet IEEE Transactions on signal processing 50 (3), 736-746, 2002	1345	2002
Fundamentals of stochastic filtering A Bain, D Crisan Springer, 2009	941	2009
The Oxford handbook of nonlinear filtering D Crisan, B Rozovskii Oxford University Press, 2011	233	2011
Discrete filtering using branching and interacting particle systems D Crisan, P Del Moral, T Lyons Université de Toulouse. Laboratoire de Statistique et Probabilités [LSP], 1998	229	1998
On the stability of sequential Monte Carlo methods in high dimensions A Beskos, D Crisan, A Jasra	206	2014
Particle filters—a theoretical perspective D Crisan Sequential Monte Carlo methods in practice, 17-41, 2001	196	2001
A mathematical theory of financial bubbles FE Benth, D Crisan, P Guasoni, K Manolarakis, J Muhle-Karbe, C Nee, ... Paris-Princeton Lectures on Mathematical Finance 2013: Editors: Vicky …, 2013	142	2013
Solution properties of a 3D stochastic Euler fluid equation D Crisan, F Flandoli, DD Holm Journal of Nonlinear Science 29, 813-870, 2019	133	2019
A probabilistic approach to classical solutions of the master equation for large population equilibria JF Chassagneux, D Crisan, F Delarue arXiv preprint arXiv:1411.3009, 2014	133	2014
Convergence of sequential Monte Carlo methods D Crisan, A Doucet Signal Processing Group, Department of Engineering, University of Cambridge …, 2000	131	2000
A particle approximation of the solution of the Kushner–Stratonovitch equation D Crisan, T Lyons Probability Theory and Related Fields 115, 549-578, 1999	127	1999
Solving backward stochastic differential equations using the cubature method: application to nonlinear pricing D Crisan, K Manolarakis SIAM Journal on Financial Mathematics 3 (1), 534-571, 2012	124	2012
Approximate McKean–Vlasov representations for a class of SPDEs D Crisan, J Xiong Stochastics An International Journal of Probability and Stochastics …, 2010	114	2010
Convergence of a branching particle method to the solution of the Zakai equation D Crisan, J Gaines, T Lyons SIAM Journal on Applied Mathematics 58 (5), 1568-1590, 1998	113	1998
Nested particle filters for online parameter estimation in discrete-time state-space Markov models D Crisan, J Miguez	105	2018
Numerically modeling stochastic Lie transport in fluid dynamics C Cotter, D Crisan, DD Holm, W Pan, I Shevchenko Multiscale Modeling & Simulation 17 (1), 192-232, 2019	101	2019
A stable particle filter for a class of high-dimensional state-space models A Beskos, D Crisan, A Jasra, K Kamatani, Y Zhou Advances in Applied Probability 49 (1), 24-48, 2017	101	2017
The Oxford Handbook of Nonlinear Fil-tering A Doucet Oxford University Press, 2011	98	2011
Nonlinear filtering and measure-valued processes D Crisan, T Lyons Probability Theory and Related Fields 109, 217-244, 1997	96	1997
Smoothing properties of McKean–Vlasov SDEs D Crisan, E McMurray Probability Theory and Related Fields 171, 97-148, 2018	94	2018

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