INFERENSI BAYESIAN DALAM WAVELET SHRINKAGE - Wahyan, Wahyan (Sekolah Pascasarjana UGM, 2003)
In this paper, we discuss the Bayesian inference in wavelet nonparametric problems. In most areas of application, there is a need for a shrinkage procedure to (i) adapt to data and (ii) use prier information. The Bayesian paradigm provides a natural terrain for both of these goals. • In 1.oavelet domain, the Bayes rules §"(d) under the squared error loss function with selecti.on prior distribution 1r(fJ) for () whose symmetric property i.e TC(B) = rc(-B), in fact mjmic 'shrinkers', such that can be solved with wavelet shrinkage. In case the prior distribution for B with symmetric property and satisfies E(B) = 0, such that the shrinkage rules to wavelet coefficients toward the prior mean can be solved.