Bayesian nonparametric inference for a nonsequential change-point problem is studied. We use a mixture of products of Dirichlet processes as our prior distribution. This allows the data before and after the change-point to be dependent, even when the change point is known. A Gibbs sampler algorithm is also proposed in order to overcome analytic difficulties in computing the posterior distributions of interest, some of which have support on the space of all distribution functions.
Bayesian hierarchical nonparametric inference for change-point problems
PETRONE, SONIA
1996
Abstract
Bayesian nonparametric inference for a nonsequential change-point problem is studied. We use a mixture of products of Dirichlet processes as our prior distribution. This allows the data before and after the change-point to be dependent, even when the change point is known. A Gibbs sampler algorithm is also proposed in order to overcome analytic difficulties in computing the posterior distributions of interest, some of which have support on the space of all distribution functions.
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