標題: Titlebook: Bayesian Inference in Wavelet-Based Models; Peter Müller,Brani Vidakovic Book 1999 Springer Science+Business Media New York 1999 Markov mo [打印本頁] 作者: 有判斷力 時間: 2025-3-21 16:58
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書目名稱Bayesian Inference in Wavelet-Based Models讀者反饋學科排名
作者: 有常識 時間: 2025-3-21 23:02 作者: obnoxious 時間: 2025-3-22 03:00
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作者: fatty-acids 時間: 2025-3-22 05:12 作者: 察覺 時間: 2025-3-22 09:42
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作者: thrombus 時間: 2025-3-22 15:36
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作者: Jargon 時間: 2025-3-22 19:03 作者: precede 時間: 2025-3-23 01:16 作者: Ebct207 時間: 2025-3-23 01:45 作者: Acetaminophen 時間: 2025-3-23 09:19
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作者: Inelasticity 時間: 2025-3-23 11:55 作者: 講個故事逗他 時間: 2025-3-23 14:50
Human Ear Recognition by Computerity estimation. The common theme in all three applications is the lack of posterior independence for the wavelet coefficients ... In contrast, most commonly considered applications of wavelet decompositions in Statistics are based on a setup which implies . independent coefficients, essentially redu作者: 整理 時間: 2025-3-23 19:52 作者: indoctrinate 時間: 2025-3-23 22:20
Human Ear Recognition by Computerlocal procedure, since wavelet coefficients characterize the local regularity of a function. Although a wavelet transform has decorrelating properties, structures in images, like edges, are never decor-related completely, and these structures appear in the wavelet coefficients. We therefore introduc作者: ADORE 時間: 2025-3-24 02:43
Ear Detection and Recognition in 2D and 3D,ctable multiresolution paradigm. In addition to providing a very natural and useful framework for modeling and processing images, Bayesian multiscale analysis is often much less computationally demanding compared to classical Markov random field models. This chapter focuses on a probabilistic graph 作者: 煩人 時間: 2025-3-24 08:10 作者: 母豬 時間: 2025-3-24 11:16 作者: 戲法 時間: 2025-3-24 16:43 作者: Ledger 時間: 2025-3-24 22:54
Lecture Notes in Statisticshttp://image.papertrans.cn/b/image/181852.jpg作者: 注意 時間: 2025-3-25 01:39 作者: 沉著 時間: 2025-3-25 04:51
978-0-387-98885-6Springer Science+Business Media New York 1999作者: 巨碩 時間: 2025-3-25 10:26 作者: 高興一回 時間: 2025-3-25 14:50
Spectral View of Wavelets and Nonlinear Regressiond power of the wavelet approach comes from a discrete spectral analysis point of view. Some of the ideas here are the same as some introduced in Chapter 1, but the different viewpoint is intended to give additional insights.作者: 腫塊 時間: 2025-3-25 15:52
Minimax Restoration and Deconvolution we study linear and non-linear diagonal estimators in an orthogonal basis. General conditions are given to build nearly minimax optimal estimators with a thresholding in an orthogonal basis. The deconvolution of bounded variation signals is studied in further details, with an application to the deblurring of satellite images.作者: Jingoism 時間: 2025-3-25 21:17 作者: archetype 時間: 2025-3-26 00:38
Bayesian Inference in Wavelet-Based Models978-1-4612-0567-8Series ISSN 0930-0325 Series E-ISSN 2197-7186 作者: frivolous 時間: 2025-3-26 06:34 作者: 小教堂 時間: 2025-3-26 11:07 作者: reserve 時間: 2025-3-26 15:50
Jennifer A. Miller,Brendan Hoover we study linear and non-linear diagonal estimators in an orthogonal basis. General conditions are given to build nearly minimax optimal estimators with a thresholding in an orthogonal basis. The deconvolution of bounded variation signals is studied in further details, with an application to the deblurring of satellite images.作者: CAPE 時間: 2025-3-26 20:39 作者: STELL 時間: 2025-3-26 23:22 作者: Hamper 時間: 2025-3-27 05:12 作者: browbeat 時間: 2025-3-27 06:08
0930-0325 ce of numerous referees to whom we are most indebted. We are also grateful to John Kimmel and the Springer-Verlag referees for considering our proposal in a ver978-0-387-98885-6978-1-4612-0567-8Series ISSN 0930-0325 Series E-ISSN 2197-7186 作者: languid 時間: 2025-3-27 13:15
MCMC Methods in Wavelet Shrinkage: Non-Equally Spaced Regression, Density and Spectral Density Estimr vanishing coefficients. This implements wavelet coefficient thresholding as a formal Bayes rule. For non-zero coefficients we introduce shrinkage by assuming normal priors. Allowing different prior variance at each level of detail we obtain level-dependent shrinkage for non-zero coefficients..We i作者: Extemporize 時間: 2025-3-27 17:06 作者: 表否定 時間: 2025-3-27 21:23
Book 1999eree and critically evaluate the papers which were submitted for inclusion in this volume. For this substantial task, we relied on the service of numerous referees to whom we are most indebted. We are also grateful to John Kimmel and the Springer-Verlag referees for considering our proposal in a ver作者: Cardioversion 時間: 2025-3-27 22:54 作者: intercede 時間: 2025-3-28 03:32 作者: cinder 時間: 2025-3-28 08:45
An Introduction to Waveletse and below the .-axis. The diminutive connotation of . suggest the function has to be well localized. Other requirements are technical and needed mostly to ensure quick and easy calculation of the direct and inverse wavelet transform.作者: etiquette 時間: 2025-3-28 11:24 作者: Restenosis 時間: 2025-3-28 15:28
Bayesian Approach to Wavelet Decomposition and Shrinkageel 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作者: 討厭 時間: 2025-3-28 22:17 作者: FLING 時間: 2025-3-28 22:53 作者: Isthmus 時間: 2025-3-29 03:21 作者: Facilities 時間: 2025-3-29 07:55 作者: Geyser 時間: 2025-3-29 11:38
Minimax Restoration and Deconvolution we study linear and non-linear diagonal estimators in an orthogonal basis. General conditions are given to build nearly minimax optimal estimators with a thresholding in an orthogonal basis. The deconvolution of bounded variation signals is studied in further details, with an application to the deb作者: insipid 時間: 2025-3-29 16:53 作者: 玷污 時間: 2025-3-29 20:19
Best Basis Representations with Prior Statistical Modelssignal. 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作者: 微不足道 時間: 2025-3-30 01:17
Modeling Dependence in the Wavelet Domainrecursive way to compute the within- and across-level covari-ances. We then show the usefulness of those findings in some of the best known applications of wavelets in statistics. Wavelet shrinkage attempts to estimate a function from noisy data. When approaching the problem from a Bayesian point of作者: 推崇 時間: 2025-3-30 05:29
MCMC Methods in Wavelet Shrinkage: Non-Equally Spaced Regression, Density and Spectral Density Estimity estimation. The common theme in all three applications is the lack of posterior independence for the wavelet coefficients ... In contrast, most commonly considered applications of wavelet decompositions in Statistics are based on a setup which implies . independent coefficients, essentially redu作者: 輕快帶來危險 時間: 2025-3-30 08:40
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作者: heirloom 時間: 2025-3-30 13:02 作者: 難管 時間: 2025-3-30 16:46 作者: 懲罰 時間: 2025-3-30 22:27 作者: Exterior 時間: 2025-3-31 04:42 作者: Celiac-Plexus 時間: 2025-3-31 08:00 作者: 新手 時間: 2025-3-31 10:34
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.作者: 出生 時間: 2025-3-31 13:52 作者: 野蠻 時間: 2025-3-31 17:45 作者: Demulcent 時間: 2025-3-31 22:24
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.作者: 恃強凌弱的人 時間: 2025-4-1 04:12
Applications of the Proposed Techniques, develop a Bayesian-based approach to the best basis problem. As illustrated by applications to signal denoising, this leads to reduced estimation errors while preserving the classical tree search algorithm.作者: allergen 時間: 2025-4-1 06:49 作者: Firefly 時間: 2025-4-1 11:47
0930-0325 ts: The first two papers introduce basic concepts. Chapters in Part II explore different approaches to prior modeling, using independent priors. Papers in the Part III discuss decision theoretic aspects of such prior models. In Part IV, some aspects of prior modeling using priors that account for de作者: Kernel 時間: 2025-4-1 15:32 作者: 辯論的終結 時間: 2025-4-1 20:36
Empirical Bayes Estimation in Wavelet Nonparametric Regressiono estimate the hyperparameters for each level of the wavelet decomposition, bypassing the usual difficulty of hyperparameter specification in the hierarchical model. The EB approach is computationally competitive with standard methods and offers improved MSE performance over several Bayes and classical estimators in a wide variety of examples.作者: 蠟燭 時間: 2025-4-1 23:09
https://doi.org/10.1007/978-3-030-64819-0ractable settings. For the RDP models, in which a multi-scale structure is achieved through simple summing of pairs of “children” into “parents,” it is observed that a sufficient condition for a particularly tractable model is that the “parent” serve as a . for the distribution of the “children.”作者: Irrigate 時間: 2025-4-2 05:46
Rapid 3D Ear Indexing and Recognition,esses. Given discrete measurements from a long-memory process, a wavelet transform leads to a set of wavelet coefficients. We show how the statistical properties of the coefficients depend on the process underlying the data and how a Bayesian approach gives rise to a natural way of including in the model information about the process itself.作者: 萬神殿 時間: 2025-4-2 10:18 作者: 懶惰人民 時間: 2025-4-2 12:14
