Numerical Bayesian Methods Applied To Signal Processing

Author : Joseph J.K. O Ruanaidh
Publisher : Springer Science & Business Media
ISBN 13 : 1461207177
Total Pages : 256 pages
Book Rating : 4.77/5 ( download)

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Download or read book Numerical Bayesian Methods Applied to Signal Processing written by Joseph J.K. O Ruanaidh and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is concerned with the processing of signals that have been sam pled and digitized. The fundamental theory behind Digital Signal Process ing has been in existence for decades and has extensive applications to the fields of speech and data communications, biomedical engineering, acous tics, sonar, radar, seismology, oil exploration, instrumentation and audio signal processing to name but a few [87]. The term "Digital Signal Processing", in its broadest sense, could apply to any operation carried out on a finite set of measurements for whatever purpose. A book on signal processing would usually contain detailed de scriptions of the standard mathematical machinery often used to describe signals. It would also motivate an approach to real world problems based on concepts and results developed in linear systems theory, that make use of some rather interesting properties of the time and frequency domain representations of signals. While this book assumes some familiarity with traditional methods the emphasis is altogether quite different. The aim is to describe general methods for carrying out optimal signal processing.

Author : J. J. Oruanaidh
Publisher :
ISBN 13 : 9783540946298
Total Pages : pages
Book Rating : 4.92/5 ( download)

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Download or read book Numerical Bayesian Methods Applied to Signal Processing written by J. J. Oruanaidh and published by . This book was released on 1996 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Author : Jean-Francois Giovannelli
Publisher : John Wiley & Sons
ISBN 13 : 1848216378
Total Pages : 322 pages
Book Rating : 4.72/5 ( download)

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Download or read book Regularization and Bayesian Methods for Inverse Problems in Signal and Image Processing written by Jean-Francois Giovannelli and published by John Wiley & Sons. This book was released on 2015-02-16 with total page 322 pages. Available in PDF, EPUB and Kindle. Book excerpt: The focus of this book is on "ill-posed inverse problems". These problems cannot be solved only on the basis of observed data. The building of solutions involves the recognition of other pieces of a priori information. These solutions are then specific to the pieces of information taken into account. Clarifying and taking these pieces of information into account is necessary for grasping the domain of validity and the field of application for the solutions built. For too long, the interest in these problems has remained very limited in the signal-image community. However, the community has since recognized that these matters are more interesting and they have become the subject of much greater enthusiasm. From the application field’s point of view, a significant part of the book is devoted to conventional subjects in the field of inversion: biological and medical imaging, astronomy, non-destructive evaluation, processing of video sequences, target tracking, sensor networks and digital communications. The variety of chapters is also clear, when we examine the acquisition modalities at stake: conventional modalities, such as tomography and NMR, visible or infrared optical imaging, or more recent modalities such as atomic force imaging and polarized light imaging.

Author : William Ng
Publisher : Library and Archives Canada = Bibliothèque et Archives Canada
ISBN 13 : 9780612977877
Total Pages : 426 pages
Book Rating : 4.70/5 ( download)

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Download or read book Advances in Wideband Array Signal Processing Using Numerical Bayesian Methods [microform] written by William Ng and published by Library and Archives Canada = Bibliothèque et Archives Canada. This book was released on 2003 with total page 426 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis focuses on joint model order detection and estimation of the parameters of interest, with applications to narrowband and wideband array signal processing in both off-line and on-line contexts. A novel data model that is capable of handling both narrowband and wideband cases with the use of an interpolation function and signal samples is proposed. In the off-line mode, Markov Chain Monte Carlo methods are applied to obtain a numerical approximation of the joint posterior distribution of the parameters under the condition that they have stationary distribution functions. On the other hand, if the distribution functions are nonstationary, the on-line approach is used. That approach employs a sequential implementation of Monte Carlo methods, applied to probabilistic dynamic systems.

Author : James V. Candy
Publisher : John Wiley & Sons
ISBN 13 : 1119125456
Total Pages : 640 pages
Book Rating : 4.57/5 ( download)

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Download or read book Bayesian Signal Processing written by James V. Candy and published by John Wiley & Sons. This book was released on 2016-07-12 with total page 640 pages. Available in PDF, EPUB and Kindle. Book excerpt: Presents the Bayesian approach to statistical signal processing for a variety of useful model sets This book aims to give readers a unified Bayesian treatment starting from the basics (Baye’s rule) to the more advanced (Monte Carlo sampling), evolving to the next-generation model-based techniques (sequential Monte Carlo sampling). This next edition incorporates a new chapter on “Sequential Bayesian Detection,” a new section on “Ensemble Kalman Filters” as well as an expansion of Case Studies that detail Bayesian solutions for a variety of applications. These studies illustrate Bayesian approaches to real-world problems incorporating detailed particle filter designs, adaptive particle filters and sequential Bayesian detectors. In addition to these major developments a variety of sections are expanded to “fill-in-the gaps” of the first edition. Here metrics for particle filter (PF) designs with emphasis on classical “sanity testing” lead to ensemble techniques as a basic requirement for performance analysis. The expansion of information theory metrics and their application to PF designs is fully developed and applied. These expansions of the book have been updated to provide a more cohesive discussion of Bayesian processing with examples and applications enabling the comprehension of alternative approaches to solving estimation/detection problems. The second edition of Bayesian Signal Processing features: “Classical” Kalman filtering for linear, linearized, and nonlinear systems; “modern” unscented and ensemble Kalman filters: and the “next-generation” Bayesian particle filters Sequential Bayesian detection techniques incorporating model-based schemes for a variety of real-world problems Practical Bayesian processor designs including comprehensive methods of performance analysis ranging from simple sanity testing and ensemble techniques to sophisticated information metrics New case studies on adaptive particle filtering and sequential Bayesian detection are covered detailing more Bayesian approaches to applied problem solving MATLAB® notes at the end of each chapter help readers solve complex problems using readily available software commands and point out other software packages available Problem sets included to test readers’ knowledge and help them put their new skills into practice Bayesian Signal Processing, Second Edition is written for all students, scientists, and engineers who investigate and apply signal processing to their everyday problems.

Author : Lei Cheng
Publisher : Springer Nature
ISBN 13 : 3031224388
Total Pages : 189 pages
Book Rating : 4.86/5 ( download)

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Download or read book Bayesian Tensor Decomposition for Signal Processing and Machine Learning written by Lei Cheng and published by Springer Nature. This book was released on 2023-02-16 with total page 189 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents recent advances of Bayesian inference in structured tensor decompositions. It explains how Bayesian modeling and inference lead to tuning-free tensor decomposition algorithms, which achieve state-of-the-art performances in many applications, including blind source separation; social network mining; image and video processing; array signal processing; and, wireless communications. The book begins with an introduction to the general topics of tensors and Bayesian theories. It then discusses probabilistic models of various structured tensor decompositions and their inference algorithms, with applications tailored for each tensor decomposition presented in the corresponding chapters. The book concludes by looking to the future, and areas where this research can be further developed. Bayesian Tensor Decomposition for Signal Processing and Machine Learning is suitable for postgraduates and researchers with interests in tensor data analytics and Bayesian methods.

Author : Niansheng Tang
Publisher : BoD – Books on Demand
ISBN 13 : 1838803858
Total Pages : 120 pages
Book Rating : 4.58/5 ( download)

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Download or read book Bayesian Inference on Complicated Data written by Niansheng Tang and published by BoD – Books on Demand. This book was released on 2020-07-15 with total page 120 pages. Available in PDF, EPUB and Kindle. Book excerpt: Due to great applications in various fields, such as social science, biomedicine, genomics, and signal processing, and the improvement of computing ability, Bayesian inference has made substantial developments for analyzing complicated data. This book introduces key ideas of Bayesian sampling methods, Bayesian estimation, and selection of the prior. It is structured around topics on the impact of the choice of the prior on Bayesian statistics, some advances on Bayesian sampling methods, and Bayesian inference for complicated data including breast cancer data, cloud-based healthcare data, gene network data, and longitudinal data. This volume is designed for statisticians, engineers, doctors, and machine learning researchers.

Author :
Publisher : Academic Press
ISBN 13 : 0323952690
Total Pages : 322 pages
Book Rating : 4.99/5 ( download)

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Download or read book Advancements in Bayesian Methods and Implementations written by and published by Academic Press. This book was released on 2022-10-06 with total page 322 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advancements in Bayesian Methods and Implementation, Volume 47 in the Handbook of Statistics series, highlights new advances in the field, with this new volume presenting interesting chapters on a variety of timely topics, including Fisher Information, Cramer-Rao and Bayesian Paradigm, Compound beta binomial distribution functions, MCMC for GLMMS, Signal Processing and Bayesian, Mathematical theory of Bayesian statistics where all models are wrong, Machine Learning and Bayesian, Non-parametric Bayes, Bayesian testing, and Data Analysis with humans, Variational inference or Functional horseshoe, Generalized Bayes. Provides the authority and expertise of leading contributors from an international board of authors Presents the latest release in the Handbook of Statistics series Updated release includes the latest information on Advancements in Bayesian Methods and Implementation

Author : Václav Šmídl
Publisher : Springer Science & Business Media
ISBN 13 : 3540288201
Total Pages : 241 pages
Book Rating : 4.06/5 ( download)

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Download or read book The Variational Bayes Method in Signal Processing written by Václav Šmídl and published by Springer Science & Business Media. This book was released on 2006-03-30 with total page 241 pages. Available in PDF, EPUB and Kindle. Book excerpt: Treating VB approximation in signal processing, this monograph is for academic and industrial research groups in signal processing, data analysis, machine learning and identification. It reviews distributional approximation, showing that tractable algorithms for parametric model identification can be generated in off-line and on-line contexts.

Author : Yosra Marnissi
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.40/5 ( download)

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Download or read book Bayesian Methods for Inverse Problems in Signal and Image Processing written by Yosra Marnissi and published by . This book was released on 2017 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian approaches are widely used in signal processing applications. In order to derive plausible estimates of original parameters from their distorted observations, they rely on the posterior distribution that incorporates prior knowledge about the unknown parameters as well as informations about the observations. The posterior mean estimator is one of the most commonly used inference rule. However, as the exact posterior distribution is very often intractable, one has to resort to some Bayesian approximation tools to approximate it. In this work, we are mainly interested in two particular Bayesian methods, namely Markov Chain Monte Carlo (MCMC) sampling algorithms and Variational Bayes approximations (VBA).This thesis is made of two parts. The first one is dedicated to sampling algorithms. First, a special attention is devoted to the improvement of MCMC methods based on the discretization of the Langevin diffusion. We propose a novel method for tuning the directional component of such algorithms using a Majorization-Minimization strategy with guaranteed convergence properties.Experimental results on the restoration of a sparse signal confirm the performance of this new approach compared with the standard Langevin sampler. Second, a new sampling algorithm based on a Data Augmentation strategy, is proposed to improve the convergence speed and the mixing properties of standard MCMC sampling algorithms. Our methodological contributions are validated on various applications in image processing showing the great potentiality of the proposed method to manage problems with heterogeneous correlations between the signal coefficients.In the second part, we propose to resort to VBA techniques to build a fast estimation algorithm for restoring signals corrupted with non-Gaussian noise. In order to circumvent the difficulties raised by the intricate form of the true posterior distribution, a majorization technique is employed to approximate either the data fidelity term or the prior density. Thanks to its flexibility, the proposed approach can be applied to a broad range of data fidelity terms allowing us to estimate the target signal jointly with the associated regularization parameter. Illustration of this approach through examples of image deconvolution in the presence of mixed Poisson-Gaussian noise, show the good performance of the proposed algorithm compared with state of the art supervised methods.