## Adaptive Bayesian wavelet

- H.A.B. CHIPMAN, E. D. KOLACZYK, R. E. MCCULLOCH
- Journal of the Royal Statistical Society,
- 1997

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)

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