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Bayesian Deconvolution of Sparse Processes: In this work, various Bayesian methods for deconvolution and blind deconvolution of sparse;
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Blind image deconvolution is constantly receiving increasing attention from the academic as well the industrial world due to both its;
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categorized as either non-Bayesian or Bayesian. A comparison of the performance and complexity of several such algorithms is given.;
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This is the first book designed to introduce Bayesian inference procedures for stochastic processes. There are clear advantages to the;
Vergelijkbare producten zoals Bayesian Inference for Stochastic Processes
This is the first book designed to introduce Bayesian inference procedures for stochastic processes. There are clear advantages to the;
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Bayesian analysis of complex models based on stochastic processes has in recent years become a growing area. This book provides a unified;
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Providing a novel approach to sparsity, this comprehensive book presents the theory of stochastic processes that are ruled by linear;
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industrial processes. Bridging the gap between application and development, this reference adopts Bayesian approaches for actual industrial;
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of Bayesian learning to include nonparametric Bayesian methods, with a focus on the Chinese restaurant and the Indian buffet processes.
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Deconvolution is a technique in signal or image processing that is applied to recover information. When it is employed, it is usually;
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These lecture notes provide a rapid, accessible introduction to Bayesian statistical methods. The course covers the fundamental philosophy;
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problems and regularized solutions using the Bayesian statistical tools, with a particular view to signal and image estimation. The first three;
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Sparse models are particularly useful in scientific applications, such as biomarker discovery in gen;...
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Sparse models are particularly useful in scientific applications, such as biomarker discovery in gen;...
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method works. The approaches include statistical parametric maps (SPM), hemodynamic response modeling and deconvolution, Bayesian, Fourier and;
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In this book you will find the use of sparse Principal Component Analysis (PCA) for representing hig;...
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Making statistical modeling and inference more accessible to ecologists and related scientists, Introduction to Hierarchical Bayesian;
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Making statistical modeling and inference more accessible to ecologists and related scientists, Introduction to Hierarchical Bayesian;
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bootstrap, Bayesian methods, and recently developed approaches. In addition, the book examines matrix decomposition, sparse multivariate analysis;
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bootstrap, Bayesian methods, and recently developed approaches. In addition, the book examines matrix decomposition, sparse multivariate analysis;
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of Bayesian classifiers, hidden Markov models, Bayesian networks, dynamic and temporal Bayesian networks, Markov random fields, influence diagrams;
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, engineers, scientists and professionals taking part in complex decision processes to successfully implement Bayesian networks, this book equips;
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Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and STAN examines the Bayesian and frequentist methods;
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Bayesian modeling with PyMC3 and exploratory analysis of Bayesian models with ArviZ Key Features A step-by-step guide to conduct Bayesian;
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Sparse grids are a popular tool for the numerical treatment of high-dimensional problems. Where clas;...
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the approximate solution of inverse problems. The resulting Approximate Deconvolution Models or ADMs have some advantages over more commonly;
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