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arXiv · 2108.12396

A class of dependent Dirichlet processes via latent multinomial processes

Luis E. Nieto-Barajas
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Abstract

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

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Luis E. Nieto-Barajas. 2021-08-27. A class of dependent Dirichlet processes via latent multinomial processes. https://arxiv.org/abs/2108.12396

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