Simulating from the posterior density of Bayesian wavelet regression estimates

Abstract

One approach is by wavelet thresholding. The vector of function values f = (f(x1); : : : ; f(xn)) can be represented by its discrete wavelet transform d = (dj;k; j = 0; : : : ; J 1; k = 0; : : : ; 2j 1). To estimate d, we compute the discrete wavelet transform b d of the data vector y and threshold these values or shrink them towards zero. The inverse DWT… (More)

Topics

3 Figures and Tables

Cite this paper

@inproceedings{BarberSimulatingFT, title={Simulating from the posterior density of Bayesian wavelet regression estimates}, author={Stuart Barber} }