Adaptive Convergence Rates of a Dirichlet Process Mixture of Multivariate Normals [article]

Surya T. Tokdar
2011 arXiv   pre-print
It is shown that a simple Dirichlet process mixture of multivariate normals offers Bayesian density estimation with adaptive posterior convergence rates. Toward this, a novel sieve for non-parametric mixture densities is explored, and its rate adaptability to various smoothness classes of densities in arbitrary dimension is demonstrated. This sieve construction is expected to offer a substantial technical advancement in studying Bayesian non-parametric mixture models based on stick-breaking priors.
arXiv:1111.4148v1 fatcat:oyeli2etzzfvjm3k5zxdd36nim