  We consider the asymptotic behavior of posterior distributions and Bayes
estimators based on observations which are required to be neither independent
nor identically distributed. We give general results on the rate of convergence
of the posterior measure relative to distances derived from a testing
criterion. We then specialize our results to independent, nonidentically
distributed observations, Markov processes, stationary Gaussian time series and
the white noise model. We apply our general results to several examples of
infinite-dimensional statistical models including nonparametric regression with
normal errors, binary regression, Poisson regression, an interval censoring
model, Whittle estimation of the spectral density of a time series and a
nonlinear autoregressive model.
