J. Cano, M. Kessler, S. Salmer?un, and D. , Approximation of the posterior density for diffusion processes, Statistics & Probability Letters, vol.76, issue.1, pp.39-44, 2006.
DOI : 10.1016/j.spl.2005.07.007

B. P. Carlin and T. A. Louis, Bayes and empirical Bayes methods for data analysis, of Monographs on Statistics and Applied Probability, 2000.

R. De-la-cruz-mesia and G. Marshall, Non-linear random effects models with continuous time autoregressive errors: a Bayesian approach, Statistics in Medicine, vol.92, issue.9, pp.1471-1484, 2006.
DOI : 10.1002/sim.2290

S. Donnet, J. Foulley, and A. Samson, Bayesian analysis of growth curves using mixed models defined by stochastic differential equations Soumis, 2009.

F. Jaffrézic, C. Meza, M. Lavielle, and J. L. Foulley, Genetic analysis of growth curves using the SAEM algorithm, Genetics Selection Evolution, vol.38, pp.583-600, 2006.

C. Meza, F. Jaffrézic, and F. , REML Estimation of Variance Parameters in Nonlinear Mixed Effects Models Using the SAEM Algorithm, Biometrical Journal, vol.85, issue.6, pp.876-888, 2007.
DOI : 10.1002/bimj.200610348

Z. Oravecz, F. Tuerlinckx, and J. Vandekerckhove, A Hierarchical Ornstein???Uhlenbeck Model for Continuous Repeated Measurement Data, Psychometrika, vol.89, issue.3
DOI : 10.1007/s11336-008-9106-8

D. Zimmerman and V. Núnez-antón, Parametric modelling of growth curve data: An overview, Test, vol.54, issue.1, pp.1-73, 2001.
DOI : 10.1007/BF02595823