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Bayesian Deconvolution of Sparse Processes

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

Blind image deconvolution is constantly receiving increasing attention from the academic as well the industrial world due to both its;

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Molecular Imaging in Nano MRI

categorized as either non-Bayesian or Bayesian. A comparison of the performance and complexity of several such algorithms is given.;

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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 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 Stochastic Process Models

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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Introduction To Sparse Stochastic Processes

Providing a novel approach to sparsity, this comprehensive book presents the theory of stochastic processes that are ruled by linear;

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Bayesian Process Monitoring, Control and Optimization

industrial processes. Bridging the gap between application and development, this reference adopts Bayesian approaches for actual industrial;

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Machine Learning

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 of Images and Spectra

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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An Introduction to Bayesian Inference, Methods and Computation

These lecture notes provide a rapid, accessible introduction to Bayesian statistical methods. The course covers the fundamental philosophy;

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Bayesian Approach to Inverse Problems

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 Modeling

Sparse models are particularly useful in scientific applications, such as biomarker discovery in gen;...

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Sparse Modeling

Sparse models are particularly useful in scientific applications, such as biomarker discovery in gen;...

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Computing Brain Activity Maps from fMRI Time-Series Images

method works. The approaches include statistical parametric maps (SPM), hemodynamic response modeling and deconvolution, Bayesian, Fourier and;

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Sparse representation of High Dimensional Data for Classification

In this book you will find the use of sparse Principal Component Analysis (PCA) for representing hig;...

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Introduction to Hierarchical Bayesian Modeling for Ecological Data

Making statistical modeling and inference more accessible to ecologists and related scientists, Introduction to Hierarchical Bayesian;

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Introduction to Hierarchical Bayesian Modeling for Ecological Data

Making statistical modeling and inference more accessible to ecologists and related scientists, Introduction to Hierarchical Bayesian;

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Statistical Learning with Sparsity

bootstrap, Bayesian methods, and recently developed approaches. In addition, the book examines matrix decomposition, sparse multivariate analysis;

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Statistical Learning With Sparsity

bootstrap, Bayesian methods, and recently developed approaches. In addition, the book examines matrix decomposition, sparse multivariate analysis;

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Probabilistic Graphical Models

of Bayesian classifiers, hidden Markov models, Bayesian networks, dynamic and temporal Bayesian networks, Markov random fields, influence diagrams;

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Bayesian Networks

, 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

Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and STAN examines the Bayesian and frequentist methods;

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Bayesian Analysis with Python

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 and Applications Miami 2016

Sparse grids are a popular tool for the numerical treatment of high-dimensional problems. Where clas;...

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Approximate Deconvolution Models of Turbulence

the approximate solution of inverse problems. The resulting Approximate Deconvolution Models or ADMs have some advantages over more commonly;

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