On Bayesian asymptotics in stochastic differential equations with random effects

Delattre et al. (2013) investigated asymptotic properties of the maximum likelihood estimator of the population parameters of the random effects associated with nn independent stochastic differential equations (SDESDE’s) assuming that the SDESDE’s are independent and identical (iidiid).In this article, we consider the Bayesian approach to learning about the population parameters, and prove consistency and asymptotic normality of the corresponding posterior distribution in the iidiid set-up as well as when the SDESDE’s are independent but non-identical.