M. Asch, M. Bocquet, and M. Nodet, Data assimilation: methods, algorithms, and applications, Fundamentals of Algorithms. SIAM, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01402885

A. Bensoussan, Estimation and Control of Dynamical Systems. Interdisciplinary Applied Mathematics, 2018.

J. Blum, F. Dimet, and I. M. Navon, Data assimilation for geophysical fluids, Handbook of Numerical Analysis: Computational Methods for the Atmosphere and the Oceans, 2008.
URL : https://hal.archives-ouvertes.fr/inria-00391892

D. Chapelle, M. Fragu, V. Mallet, and P. Moireau, Fundamental principles of data assimilation underlying the Verdandi library: applications to biophysical model personalization within euHeart, Medical & Biological Eng & Computing, vol.51, pp.1221-1233, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00760887

E. Comets, A. Lavenu, and M. Lavielle, Parameter estimation in nonlinear mixed effect models using saemix, an R implementation of the SAEM algorithm, Journal of Statistical Software, 2016.
URL : https://hal.archives-ouvertes.fr/inserm-01502767

M. Delattre and M. Lavielle, Coupling the SAEM algorithm and the extended Kalman filter for maximum likelihood estimation in mixed-effects diffusion models, Statistics and its interface, vol.6, issue.4, pp.519-532, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00916803

M. J. Denwood, runjags: An R package providing interface utilities, model templates, parallel computing methods and additional distributions for MCMC models in JAGS, Journal of Statistical Software, vol.71, issue.9, pp.1-25, 2016.

S. B. Duffull, C. M. Kirkpatrick, B. Green, and N. H. Holford, Analysis of population pharmacokinetic data using NONMEM and WinBUGS, Journal of Biopharmaceutical Statistics, vol.15, issue.1, pp.53-73, 2004.

G. Evensen, Data Assimilation -The Ensemble Kalman Filter, 2007.

S. J. Julier and J. K. Uhlmann, A new extension of the Kalman filter to nonlinear systems, Proc. of AeroSense: The 11th Int. Symp. on Aerospace/Defence Sensing, Simulation and Controls, 1997.

R. Kalman and R. Bucy, New results in linear filtering and prediction theory, Trans. ASME J. Basic. Eng, vol.83, pp.95-108, 1961.

S. Klim, S. B. Mortensen, N. R. Kristensen, R. V. Overgaard, and H. Madsen, Population stochastic modelling (PSM)-an R package for mixed-effects models based on stochastic differential equations. Computer methods and programs in biomedicine, vol.94, pp.279-289, 2009.

E. Kuhn and M. Lavielle, Maximum likelihood estimation in nonlinear mixed effects models, Computational Statistics & Data Analysis, vol.49, issue.4, pp.1020-1038, 2005.

X. Liu and Y. Wang, Comparing the performance of [FOCE] and different expectation-maximization methods in handling complex population physiologically-based pharmacokinetic models, Journal of pharmacokinetics and pharmacodynamics, vol.43, issue.4, pp.359-370, 2016.

P. Moireau, A Discrete-time Optimal Filtering Approach for Non-linear Systems as a Stable Discretization of the Mortensen Observer. ESAIM: Control, Optimisation and Calculus of Variations, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01671271

P. Moireau and D. Chapelle, Reduced-order unscented Kalman filtering with application to parameter identification in large-dimensional systems. ESAIM: Control, Optimisation and Calculus of Variations, vol.17, pp.380-405, 2011.
URL : https://hal.archives-ouvertes.fr/inria-00550104

R. V. Overgaard, N. Jonsson, C. W. Tornøe, and H. Madsen, Non-linear mixed-effects models with stochastic differential equations: implementation of an estimation algorithm, Journal of pharmacokinetics and pharmacodynamics, vol.32, issue.1, pp.85-107, 2005.

A. S. Perelson, A. U. Neumann, M. Markowitz, J. M. Leonard, and D. D. Ho, HIV-1 dynamics in vivo: virion clearance rate, infected cell life-span, and viral generation time, Science, vol.271, issue.5255, pp.1582-1586, 1996.

D. T. Pham, J. Verron, and C. M. Roubaud, A singular evolutive extended Kalman filter for data assimilation in oceanography, Journal of Marine systems, vol.16, issue.3-4, pp.323-340, 1998.

D. T. Pham, Stochastic methods for sequential data assimilation in strongly nonlinear systems, Monthly Weather Review, vol.129, issue.5, pp.1194-1207, 2001.
URL : https://hal.archives-ouvertes.fr/inria-00073082

J. C. Pinheiro and D. M. Bates, Approximations to the log-likelihood function in the nonlinear mixed-effects model, Journal of computational and Graphical Statistics, vol.4, issue.1, pp.12-35, 1995.

E. L. Plan, A. Maloney, F. Mentré, M. O. Karlsson, and J. Bertrand, Performance comparison of various maximum likelihood nonlinear mixed-effects estimation methods for dose-response models, The AAPS journal, vol.14, issue.3, pp.420-432, 2012.
URL : https://hal.archives-ouvertes.fr/inserm-00709829

M. Prague, Use of dynamical models for treatment optimization in HIV infected patients: a sequential bayesian analysis approach, Journal de la Société Française de Statistique, vol.157, issue.2, p.20, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01416102

M. Prague, D. Commenges, J. Guedj, J. Drylewicz, and R. Thiébaut, NIMROD : A program for inference via a normal approximation of the posterior in models with random effects based on ordinary differential equations. Computer methods and programs in biomedicine, vol.111, pp.447-458, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00933752

D. Simon, Optimal State Estimation: Kalman, H ? , and Nonlinear Approaches, 2006.

C. W. Tornøe, R. V. Overgaard, H. Agersø, H. Nielsen, H. A. Madsen et al., Stochastic differential equations in nonmem R : implementation, application, and comparison with ordinary differential equations, Pharmaceutical research, vol.22, issue.8, pp.1247-1258, 2005.

R. A. Upton, Pharmacokinetic interactions between theophylline and other medication (Part I), Clinical pharmacokinetics, vol.20, issue.1, pp.66-80, 1991.

G. Verbeke, Linear mixed models for longitudinal data, Linear mixed models in practice, pp.63-153, 1997.

J. Wakefield and A. Racine-poon, An application of bayesian population pharmacokinetic/pharmacodynamic models to dose recommendation, Statistics in medicine, vol.14, issue.9, pp.971-986, 1995.

H. Wu, Statistical methods for HIV dynamic studies in aids clinical trials. Statistical methods in medical research, vol.14, pp.171-192, 2005.