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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https://doi.org/10.1007/978-981-13-6332-0el for its wavelet coefficients by establishing a relationship between the hyperparameters of the proposed model and the parameters of those Besov spaces within which realizations from the prior will fall. Such a relation may be seen as giving insight into the meaning of the Besov space parameters t

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Women: Facing the Challenge of Migration,for the piecewise constant Haar wavelet basis, then extended to using smooth wavelet bases. Although developed initially for use in the standard change-point model, the analysis can be applied to the problem of estimating the location of a discontinuity in an otherwise smooth function by considering

thrombus

https://doi.org/10.1007/978-3-319-66957-1distribution. Elicitation in the wavelet domain is considered by first describing the structure of a wavelet model, and examining several prior distributions that are used in a variety of recent articles. Although elicitation has not been directly considered in many of these papers, most do attach s

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Applications of the Proposed Techniques,signal. Applying these deterministic search techniques to stochastic signals may, however, lead to statistically unreliable results. In this chapter, we revisit this problem and introduce prior models on the underlying signal in noise. We propose several techniques to derive the prior parameters and