Bayesian Modelling in the wavelet domain Export

@unpublished{citeulike:409738, abstract = {Wavelets are the building blocks of wavelet transforms the same way that the functions e inx are the building blocks of the ordinary Fourier transform. But in contrast to sines and cosines, wavelets can be (or almost can be) sup- ported on an arbitrarily small closed interval. This feature makes wavelets a very powerful tool in dealing with phenomena that change rapidly in time. In many statistical applications, there is a need for procedures to (i) adapt to data and (ii) use prior information. The interface of wavelets and the Bayesian paradigm provides a natural terrain for both of these goals. In this chapter, the authors provide an overview of the current status of research involving Bayesian inference in wavelet nonparametric problems. Two applications, one in functional data analysis (FDA) and the second in geoscience are discussed in more detail.}, author = {Ruggeri and Vidakovic}, citeulike-article-id = {409738}, keywords = {approximation}, posted-at = {2005-11-27 21:12:18}, priority = {2}, title = {Bayesian Modelling in the wavelet domain}, year = {2005} }

TY - UNPB ID - citeulike:409738 L3 - citeulike-article-id:409738 N2 - Wavelets are the building blocks of wavelet transforms the same way that the functions e inx are the building blocks of the ordinary Fourier transform. But in contrast to sines and cosines, wavelets can be (or almost can be) sup- ported on an arbitrarily small closed interval. This feature makes wavelets a very powerful tool in dealing with phenomena that change rapidly in time. In many statistical applications, there is a need for procedures to (i) adapt to data and (ii) use prior information. The interface of wavelets and the Bayesian paradigm provides a natural terrain for both of these goals. In this chapter, the authors provide an overview of the current status of research involving Bayesian inference in wavelet nonparametric problems. Two applications, one in functional data analysis (FDA) and the second in geoscience are discussed in more detail. TI - Bayesian Modelling in the wavelet domain KW - approximation AU - Ruggeri AU - Vidakovic PY - 2005/// ER -