C. Andrieu, A. Doucet, and R. Holenstein, Particle Markov chain Monte Carlo methods, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.50, issue.3, pp.269-342, 2010.
DOI : 10.1214/06-BA127

URL : http://onlinelibrary.wiley.com/doi/10.1111/j.1467-9868.2009.00736.x/pdf

T. Berry and T. Sauer, Adaptive ensemble Kalman filtering of non-linear systems, Tellus A: Dynamic Meteorology and Oceanography, vol.60, issue.1, pp.20-331, 2013.
DOI : 10.1175/1520-0469(2003)060<1140:ACOBAE>2.0.CO;2

O. Cappé, S. Godsill, and E. Moulines, An Overview of Existing Methods and Recent Advances in Sequential Monte Carlo, Proceedings of the IEEE, vol.95, issue.5, pp.899-924, 2007.
DOI : 10.1109/JPROC.2007.893250

A. Carrassi, M. Bocquet, L. Bertino, and G. Evensen, Data assimilation in the geosciences-an overview on methods, issues and perspectives. arXiv preprint, 2017.

G. Celeux, D. Chauveau, and J. Diebolt, On Stochastic Versions of the EM Algorithm, 1995.
URL : https://hal.archives-ouvertes.fr/inria-00074164

K. Chan and J. Ledolter, Monte Carlo EM Estimation for Time Series Models Involving Counts, Journal of the American Statistical Association, vol.75, issue.429, pp.242-252, 1995.
DOI : 10.1093/biomet/75.4.621

N. Chopin and S. Singh, On particle Gibbs sampling, Bernoulli, vol.21, issue.3, pp.1855-1883, 2015.
DOI : 10.3150/14-BEJ629

URL : http://arxiv.org/pdf/1304.1887

B. Delyon, M. Lavielle, and E. Moulines, Convergence of a stochastic approximation version of the em algorithm Annals of statistics, pp.94-128, 1999.

A. Dempster, N. Laird, and D. Rubin, Maximum likelihood from incomplete data via the em algorithm, Journal of the Royal Statistical Society. Series BMethodological), vol.39, issue.1, pp.1-38, 1977.

G. Desroziers, L. Berre, B. Chapnik, and P. Poli, Diagnosis of observation, background and analysis-error statistics in observation space, Quarterly Journal of the Royal Meteorological Society, vol.75, issue.613, pp.3385-3396, 2005.
DOI : 10.1256/qj.05.108

R. Douc and O. Cappé, Comparison of resampling schemes for particle filtering, ISPA 2005. Proceedings of the 4th International Symposium on Image and Signal Processing and Analysis, 2005., pp.64-69, 2005.
DOI : 10.1109/ISPA.2005.195385

URL : https://hal.archives-ouvertes.fr/hal-00005883

R. Douc, A. Garivier, E. Moulines, and J. Olsson, On the forward filtering backward smoothing particle approximations of the smoothing distribution in general state spaces models. arXiv preprint, 2009.
URL : https://hal.archives-ouvertes.fr/hal-00370685

A. Doucet, N. De-freitas, and N. Gordon, Sequential monte carlo methods in practice, Statistics for Engineering and Information Science, pp.978-979, 2001.
DOI : 10.1007/978-1-4757-3437-9

A. Doucet, S. Godsill, and C. Andrieu, On Sequential Monte Carlo Sampling Methods for Bayesian Filtering, 1998.

A. Doucet and A. Johansen, A tutorial on particle filtering and smoothing: Fifteen years later. Handbook of nonlinear filtering, pp.656-704, 2009.

D. Dreano, P. Tandeo, M. Pulido, B. Ait-el-fquih, T. Chonavel et al., Estimating model-error covariances in nonlinear state-space models using Kalman smoothing and the expectation-maximization algorithm, Quarterly Journal of the Royal Meteorological Society, vol.136, issue.705, pp.1877-1885, 2017.
DOI : 10.1002/qj.2803

G. Evensen, V. Leeuwen, and P. , An Ensemble Kalman Smoother for Nonlinear Dynamics, Monthly Weather Review, vol.128, issue.6, pp.1852-1867, 2000.
DOI : 10.1175/1520-0493(2000)128<1852:AEKSFN>2.0.CO;2

M. Ghil and P. Malanotte-rizzoli, Data assimilation in meteorology and oceanography. Advances in geophysics 33, pp.141-266, 1991.

S. Godsill, A. Doucet, and M. West, Monte Carlo Smoothing for Nonlinear Time Series, Journal of the American Statistical Association, vol.99, issue.465, pp.156-168, 2004.
DOI : 10.1198/016214504000000151

J. Hol, T. Schon, and F. Gustafsson, On Resampling Algorithms for Particle Filters, 2006 IEEE Nonlinear Statistical Signal Processing Workshop, pp.79-82, 2006.
DOI : 10.1109/NSSPW.2006.4378824

N. Kantas, A. Doucet, S. Singh, J. Maciejowski, and N. Chopin, On Particle Methods for Parameter Estimation in State-Space Models, Statistical Science, vol.30, issue.3, pp.328-351, 2015.
DOI : 10.1214/14-STS511

G. Kitagawa, A Self-Organizing State-Space Model, Journal of the American Statistical Association, vol.93, issue.443, pp.1203-1215, 1998.
DOI : 10.2307/2669862

J. Kokkala, A. Solin, and S. Särkkä, Expectation maximization based parameter estimation by sigma-point and particle smoothing, pp.1-8, 2014.

E. Kuhn and M. Lavielle, Coupling a stochastic approximation version of EM with an MCMC procedure, ESAIM: Probability and Statistics, vol.34, pp.115-131, 2004.
DOI : 10.1080/02331880008802704

L. Gland, F. Monbet, V. Tran, and V. , Large sample asymptotics for the ensemble kalman filter, 2009.
URL : https://hal.archives-ouvertes.fr/inria-00409060

R. Lguensat, P. Tandeo, P. Ailliot, M. Pulido, and R. Fablet, The Analog Data Assimilation, Monthly Weather Review, vol.145, issue.10, pp.4093-4107, 2017.
DOI : 10.1175/MWR-D-16-0441.s1

URL : https://hal.archives-ouvertes.fr/hal-01609141

F. Lindsten, An efficient stochastic approximation EM algorithm using conditional particle filters, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp.6274-6278, 2013.
DOI : 10.1109/ICASSP.2013.6638872

URL : http://users.isy.liu.se/en/rt/schon/Publications/LindstenSJ2013.pdf

F. Lindsten, M. Jordan, and T. Schön, Particle gibbs with ancestor sampling, Journal of Machine Learning Research, vol.15, pp.2145-2184, 2014.

F. Lindsten, T. Schön, and M. Jordan, Ancestor sampling for particle gibbs, Advances in Neural Information Processing Systems, pp.2591-2599, 2012.

F. Lindsten and T. Schön, On the use of backward simulation in particle markov chain monte carlo methods. arXiv preprint, 2011.

F. Lindsten and T. Schön, On the use of backward simulation in the particle Gibbs sampler, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.3845-3848, 2012.
DOI : 10.1109/ICASSP.2012.6288756

F. Lindsten and T. Schön, Backward Simulation Methods for Monte Carlo Statistical Inference, Foundations and Trends?? in Machine Learning, vol.6, issue.1, pp.1-143, 2013.
DOI : 10.1561/2200000045

E. Lorenz, Deterministic Nonperiodic Flow, Journal of the Atmospheric Sciences, vol.20, issue.2, pp.130-141, 1963.
DOI : 10.1175/1520-0469(1963)020<0130:DNF>2.0.CO;2

G. Mclachlan and T. Krishnan, The em algorithm and extensions, 2007.

T. Miyoshi, The Gaussian Approach to Adaptive Covariance Inflation and Its Implementation with the Local Ensemble Transform Kalman Filter, Monthly Weather Review, vol.139, issue.5, pp.1519-1535, 2011.
DOI : 10.1175/2010MWR3570.1

J. Olsson, O. Cappé, R. Douc, and E. Moulines, Sequential Monte Carlo smoothing with application to parameter estimation in nonlinear state space models, Bernoulli, vol.14, issue.1, pp.155-179, 2008.
DOI : 10.3150/07-BEJ6150

URL : https://hal.archives-ouvertes.fr/hal-00096080

U. Picchini and A. Samson, Coupling stochastic EM and approximate Bayesian computation for parameter inference in state-space models, Computational Statistics, vol.66, issue.1, pp.179-212, 2018.
DOI : 10.1103/PhysRevE.66.016210

URL : https://hal.archives-ouvertes.fr/hal-01623737

M. Pitt and N. Shephard, Filtering via Simulation: Auxiliary Particle Filters, Journal of the American Statistical Association, vol.24, issue.446, pp.590-599, 1999.
DOI : 10.1016/0005-1098(71)90097-5

J. Poterjoy and J. Anderson, Efficient Assimilation of Simulated Observations in a High-Dimensional Geophysical System Using a Localized Particle Filter, Monthly Weather Review, vol.144, issue.5, pp.2007-2020, 2016.
DOI : 10.1175/MWR-D-15-0322.1

M. Pulido, P. Tandeo, M. Bocquet, A. Carrassi, and M. Lucini, Stochastic parameterization identification using ensemble kalman filtering combined with expectation-maximization and newton-raphson maximum likelihood methods. arXiv preprint, 2017.
DOI : 10.1080/16000870.2018.1442099

URL : https://doi.org/10.1080/16000870.2018.1442099

T. Schön, A. Wills, and B. Ninness, System identification of nonlinear state-space models, Automatica, vol.47, issue.1, pp.39-49, 2011.
DOI : 10.1016/j.automatica.2010.10.013

R. Shumway and D. Stoffer, AN APPROACH TO TIME SERIES SMOOTHING AND FORECASTING USING THE EM ALGORITHM, Journal of Time Series Analysis, vol.23, issue.4, pp.253-264, 1982.
DOI : 10.1080/03610928108828137

C. Snyder, Particle filters, the optimal proposal and high-dimensional systems, Proceedings of the ECMWF Seminar on Data Assimilation for atmosphere and ocean, pp.1-10, 2011.

J. Stroud and T. Bengtsson, Sequential State and Variance Estimation within the Ensemble Kalman Filter, Monthly Weather Review, vol.135, issue.9, pp.3194-3208, 2007.
DOI : 10.1175/MWR3460.1

J. Stroud, M. Katzfuss, and C. Wikle, A Bayesian Adaptive Ensemble Kalman Filter for Sequential State and Parameter Estimation, Monthly Weather Review, vol.146, issue.1, 2017.
DOI : 10.1175/MWR-D-16-0427.1

A. Svensson, T. Schön, and M. Kok, Nonlinear state space smoothing using the conditional particle filter, 2015.

P. Tandeo, P. Ailliot, J. Ruiz, A. Hannart, B. Chapron et al., Combining Analog Method and Ensemble Data Assimilation: Application to the Lorenz-63 Chaotic System, Machine Learning and Data Mining Approaches to Climate Science, pp.3-12
DOI : 10.1007/978-3-319-17220-0_1

URL : https://hal.archives-ouvertes.fr/hal-01202496

P. Tandeo, M. Pulido, and F. Lott, Offline parameter estimation using EnKF and maximum likelihood error covariance estimates: Application to a subgrid-scale orography parametrization, Quarterly Journal of the Royal Meteorological Society, vol.61, issue.687, pp.383-395, 2015.
DOI : 10.1111/j.1600-0870.2009.00407.x

G. Ueno, T. Higuchi, T. Kagimoto, and N. Hirose, Maximum likelihood estimation of error covariances in ensemble-based filters and its application to a coupled atmosphere-ocean model, Quarterly Journal of the Royal Meteorological Society, vol.26, issue.650, pp.1316-1343, 2010.
DOI : 10.1137/1.9780898718003

G. Ueno and N. Nakamura, Iterative algorithm for maximum-likelihood estimation of the observation-error covariance matrix for ensemble-based filters, Quarterly Journal of the Royal Meteorological Society, vol.26, issue.678, pp.295-315, 2014.
DOI : 10.1007/s00376-009-0359-7

G. Ueno and N. Nakamura, Bayesian estimation of the observation-error covariance matrix in ensemble-based filters, Quarterly Journal of the Royal Meteorological Society, vol.26, issue.698, pp.2055-2080, 2016.
DOI : 10.1007/s00376-009-0359-7

P. Van-leeuwen, Nonlinear Data Assimilation for high-dimensional systems, In: Nonlinear Data Assimilation, pp.1-73, 2015.
DOI : 10.1007/978-3-319-18347-3_1

D. Work, S. Blandin, O. Tossavainen, B. Piccoli, and A. Bayen, A Traffic Model for Velocity Data Assimilation, Applied Mathematics Research eXpress, vol.39, issue.1, pp.1-35, 2010.
DOI : 10.1287/opre.4.1.42

Y. Zhen and J. Harlim, Adaptive error covariances estimation methods for ensemble Kalman filters, Journal of Computational Physics, vol.294, pp.619-638, 2015.
DOI : 10.1016/j.jcp.2015.03.061

M. Zhu, P. Van-leeuwen, and W. Zhang, Estimating model error covariances using particle filters, Quarterly Journal of the Royal Meteorological Society, vol.142, 2017.
DOI : 10.1002/qj.2784

URL : http://onlinelibrary.wiley.com/doi/10.1002/qj.3132/pdf