Bayesian Inference with Wavelets: Density Estimation Academic Article uri icon

abstract

  • [We propose a prior probability model in the wavelet coefficient space. The proposed model implements wavelet coefficient thresholding by full posterior inference in a coherent probability model. We introduce a prior probability model with mixture priors for the wavelet coefficients. The prior includes a positive prior probability mass at zero which leads to a posteriori thresholding and generally to a posteriori shrinkage on the coefficients. We discuss an efficient posterior simulation scheme to implement inference in the proposed model. The discussion is focused on the density estimation problem. However, the introduced prior probability model on the wavelet coefficient space and the Markov chain Monte Carlo scheme are general.]

published proceedings

  • Journal of Computational and Graphical Statistics

author list (cited authors)

  • Mller, P., Vidakovic, B., & Muller, P.

publication date

  • January 1, 1998 11:11 AM

publisher