  In the last two decades, several methods based on sequential Monte Carlo
(SMC) and Markov chain Monte Carlo (MCMC) have been proposed for Bayesian
identification of stochastic non-linear state-space models (SSMs). It is well
known that the performance of these simulation based identification methods
depends on the numerical approximations used in their design. We propose the
use of posterior Cram\'er-Rao lower bound (PCRLB) as a mean square error (MSE)
bound. Using PCRLB, a systematic procedure is developed to analyse the
estimates delivered by Bayesian identification methods in terms of bias, MSE,
and efficiency. The efficacy and utility of the proposed approach is
illustrated through a numerical example.
