Titlebook: Bayesian Inference in Wavelet-Based Models; Peter Müller,Brani Vidakovic Book 1999 Springer Science+Business Media New York 1999 Markov mo

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Empirical Bayesian Spatial Prediction Using Waveletsng, especially when the underlying signal has a sparse wavelet representation. Wavelet shrinkage based on the Bayesian approach involves specifying a prior distribution for the wavelet coefficients. In this chapter, we consider a Gaussian prior with . means for wavelet coefficients, which is differe

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https://doi.org/10.1007/978-981-13-6332-0hemselves. Furthermore, we consider Bayesian wavelet-based function estimation that gives rise to different types of wavelet shrinkage in non-parametric regression. Finally, we discuss an extension of the proposed Bayesian model by considering random functions generated by an overcomplete wavelet dictionary.

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https://doi.org/10.1007/978-3-319-66957-1hat estimators using basis averaging outperform estimators using a single basis and also estimators that first select the basis having the highest posterior probability and then estimate the unknown regression function using that basis.