Bayes Wavelet Shrinkage
نویسنده
چکیده
Wavelet shrinkage is a novel method for data denoising and function estimation. M uller and Vidakovic (1995) propose a hierarchical prior on the wavelet coeecients and shrink them by applying the induced Bayes rule. In this paper, a diierent and more elastic hierarchical prior is elicited on the model parameters describing the wavelet coeecients. Exact Bayesian analysis is impossible and the shrinkage is performed through a Markov chain Monte Carlo scheme. An application on a noisy signal is presented.
منابع مشابه
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تاریخ انتشار 2007