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We describe a procedure to introduce general dependence structures on a setof Dirichlet processes. Dependence can be in one direction to define a timeseries or in two directions to define spatial dependencies. More directions canalso be considered. Dependence is induced via a set of latent processes andexploit the conjugacy property between the Dirichlet and the multinomialprocesses to ensure that the marginal law for each element of the set is aDirichlet process. Dependence is characterised through the correlation betweenany two elements. Posterior distributions are obtained when we use the set ofDirichlet processes as prior distributions in a bayesian nonparametric context.Posterior predictive distributions induce partially exchangeable sequencesdefined by generalised P\'olya urs. A numerical example to illustrate is alsoincluded.

A class of dependent Dirichlet processes via latent multinomial processes