Convergence of a Branching Particle Method to the Solution of the Zakai Equation. - Dan Crisan; Jessica G. Gaines; Terry J. Lyons (SIAM J. Appl. Math., 1998)

Particle Filters - A Theoretical Perspective. - Dan Crisan (Sequential Monte Carlo Methods in Practice, 2001)

Minimal Entropy Approximations and Optimal Algorithms. - Dan Crisan; Terry J. Lyons (Monte Carlo Methods Appl., 2002)

A survey of convergence results on particle filtering methods for practitioners. - Dan Crisan; Arnaud Doucet (IEEE Trans. Signal Process., 2002)

Generalised particle filters with Gaussian measures. - Dan Crisan; Kai Li (EUSIPCO, 2011)

On the convergence of two sequential Monte Carlo methods for maximum a posteriori sequence estimation and stochastic global optimization. - Joaquín Míguez; Dan Crisan; Petar M. Djuric (Stat. Comput., 2013)

The stochastic filtering problem: a brief historical account. - Dan Crisan (J. Appl. Probab., 2014)

Particle filtering for Bayesian parameter estimation in a high dimensional state space model. - Joaquín Míguez; Dan Crisan; Inés P. Mariño (EUSIPCO, 2015)

On the performance of parallelisation schemes for particle filtering. - Dan Crisan; Joaquín Míguez; Gonzalo Rios-Muñoz (EURASIP J. Adv. Signal Process., 2018)

Data assimilation for a quasi-geostrophic model with circulation-preserving stochastic transport noise. - Colin J. Cotter; Dan Crisan; Darryl D. Holm; Wei Pan 006; Igor Shevchenko (CoRR, 2019)

Optimization Based Methods for Partially Observed Chaotic Systems. - Daniel Paulin; Ajay Jasra; Dan Crisan; Alexandros Beskos (Found. Comput. Math., 2019)

Solution Properties of a 3D Stochastic Euler Fluid Equation. - Dan Crisan; Franco Flandoli; Darryl D. Holm (J. Nonlinear Sci., 2019)

Numerically Modeling Stochastic Lie Transport in Fluid Dynamics. - Colin J. Cotter; Dan Crisan; Darryl D. Holm; Wei Pan 006; Igor Shevchenko (Multiscale Model. Simul., 2019)

A Particle Filter for Stochastic Advection by Lie Transport: A Case Study for the Damped and Forced Incompressible Two-Dimensional Euler Equation. - Colin J. Cotter; Dan Crisan; Darryl D. Holm; Wei Pan 006; Igor Shevchenko (SIAM/ASA J. Uncertain. Quantification, 2020)

Stable Approximation Schemes for Optimal Filters. - Dan Crisan; Alberto López-Yela; Joaquín Míguez (SIAM/ASA J. Uncertain. Quantification, 2020)

Parallel sequential Monte Carlo for stochastic gradient-free nonconvex optimization. - Ömer Deniz Akyildiz; Dan Crisan; Joaquín Míguez (Stat. Comput., 2020)

Pathwise approximations for the solution of the non-linear filtering problem. - Dan Crisan; Alexander Lobbe; Salvador Ortiz-Latorre (CoRR, 2021)

Particle representation for the solution of the filtering problem. Application to the error expansion of filtering discretizations. - Dan Crisan; Thomas G. Kurtz; Salvador Ortiz-Latorre (CoRR, 2021)

Score-Based Parameter Estimation for a Class of Continuous-Time State Space Models. - Alexandros Beskos; Dan Crisan; Ajay Jasra; Nikolas Kantas; Hamza M. Ruzayqat (SIAM J. Sci. Comput., 2021)

An application of the splitting-up method for the computation of a neural network representation for the solution for the filtering equations. - Dan Crisan; Alexander Lobbe; Salvador Ortiz-Latorre (CoRR, 2022)

Space-sequential particle filters for high-dimensional dynamical systems described by stochastic differential equations. - Deniz Akyildiz; Dan Crisan; Joaquín Míguez (CoRR, 2022)

Theoretical analysis and numerical approximation for the stochastic thermal quasi-geostrophic model. - Dan Crisan; Darryl D. Holm; Oana Lang; Prince Romeo Mensah; Wei Pan 006 (CoRR, 2022)

Bayesian inference for fluid dynamics: A case study for the stochastic rotating shallow water model. - Oana Lang; Peter Jan van Leeuwen; Dan Crisan; Roland Potthast (Frontiers Appl. Math. Stat., 2022)

A 4D-Var method with flow-dependent background covariances for the shallow-water equations. - Daniel Paulin; Ajay Jasra; Alexandros Beskos; Dan Crisan (Stat. Comput., 2022)

A Lagged Particle Filter for Stable Filtering of Certain High-Dimensional State-Space Models. - Hamza M. Ruzayqat; Aimad Er-Raiy; Alexandros Beskos; Dan Crisan; Ajay Jasra; Nikolas Kantas (SIAM/ASA J. Uncertain. Quantification, 2022)

Uniform in time convergence of numerical schemes for stochastic differential equations via Strong Exponential stability: Euler methods, Split-Step and Tamed Schemes. - Letizia Angeli; Dan Crisan; Michela Ottobre (CoRR, 2023)

Comparison of Stochastic Parametrization Schemes using Data Assimilation on Triad Models. - Bertrand Chapron; Dan Crisan; Darryl D. Holm; Oana Lang; Alexander Lobbe; Étienne Mémin (CoRR, 2023)

Unbiased estimation using a class of diffusion processes. - Hamza M. Ruzayqat; Alexandros Beskos; Dan Crisan; Ajay Jasra; Nikolas Kantas (J. Comput. Phys., 2023)

Modelling Multiperiod Carbon Markets Using Singular Forward-Backward SDEs. - Jean-François Chassagneux; Hinesh Chotai; Dan Crisan (Math. Oper. Res., 2023)

Theoretical and Computational Analysis of the Thermal Quasi-Geostrophic Model. - Dan Crisan; Darryl D. Holm; Erwin Luesink; Prince Romeo Mensah; Wei Pan 006 (J. Nonlinear Sci., 2023)

Data assimilation for the stochastic Camassa-Holm equation using particle filtering: a numerical investigation. - Colin John Cotter; Dan Crisan; Maneesh Kumar Singh 003 (CoRR, 2024)

Generative Modelling of Stochastic Rotating Shallow Water Noise. - Dan Crisan; Oana Lang; Alexander Lobbe (CoRR, 2024)

Bayesian inference for geophysical fluid dynamics using generative models. - Alexander Lobbe; Dan Crisan; Oana Lang (CoRR, 2024)

Sequential Discretization Schemes for a Class of Stochastic Differential Equations and their Application to Bayesian Filtering. - Ömer Deniz Akyildiz; Dan Crisan; Joaquín Míguez (SIAM J. Numer. Anal., 2024)

Data assimilation using a global Girsanov nudged particle filter. - Maneesh Kumar Singh 003; Joshua Hope-Collins; Colin J. Cotter; Dan Crisan (CoRR, 2025)

Nudging state-space models for Bayesian filtering under misspecified dynamics. - Fabián González; Ömer Deniz Akyildiz; Dan Crisan; Joaquín Míguez (Stat. Comput., 2025)