We present a class of Bayesian copula models whose major components are the marginal(limiting)distribution of a stationary time series and the internal dynamics of the series.We argue that these are the two features with which an analyst is typically most familiar,and hence that these are natural components with which to work.For the marginal distribution,we use a nonparametric Bayesian prior distribution along with a CDF‐inverse CDF transformation to obtain large support.