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Bayesian and grAphical Models for Biomedical Imaging

This book constitutes the refereed proceedings of the First International Workshop on Bayesian and grAphical Models for Biomedical Imaging;

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Probabilistic Graphical Models

graphical models in Python. The book covers the fundamentals for each of the main classes of PGMs, including representation, inference and;

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Probabilistic Graphical Models

graphical models in Python. The book covers the fundamentals for each of the main classes of PGMs, including representation, inference and;

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Probabilistic Graphical Models

graphical models and causal models; provides exercises, suggestions for further reading, and ideas for research or programming projects at the end;

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Advanced Methodologies for Bayesian Networks

reviewed and selected from numerous submissions. In the International Workshop on Advanced Methodologies for Bayesian Networks (AMBN), the;

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Bayesian Networks and Decision Graphs

This is a brand new edition of an essential work on Bayesian networks and decision graphs. It is an introduction to probabilistic graphical;

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Graphical Models with R

handled within R. Topics covered in the seven chapters include graphical models for contingency tables, Gaussian and mixed graphical models;

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Learning Probabilistic Graphical Models in R

* Predict and use a probabilistic graphical models (PGM) as an expert system * Comprehend how your computer can learn Bayesian modeling to solve;

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Statistical Evaluation of Diagnostic Performance

including monotone-transformation techniques in parametric ROC analysis, ROC methods for combined and pooled biomarkers, Bayesian hierarchical;

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Computer Methods, Imaging and Visualization in Biomechanics and Biomedical Engineering

approaches, FEM models, and high-resolution imaging and real-time visualization methods applied for biomedical purposes. Given the scope of its;

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Visual Analysis of Behaviour

connecting-the-dots for abnormal behaviour detection; discusses Bayesian information criterion, Bayesian networks, bag-of-words representation;

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Graphs in Biomedical Image Analysis, Computational Anatomy and Imaging Genetics

medical image analysis methods and applications, including probabilistic graphical models, neuroimaging using graph representations, machine;

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Statistical Computing in Nuclear Imaging

be used as a textbook that provides an introduction to Bayesian statistics and advanced computing in medical imaging for physicists;

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Statistical Computing in Nuclear Imaging

textbook that provides an introduction to Bayesian statistics and advanced computing in medical imaging for physicists, mathematicians, engineers;

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Multi-Modality Imaging

) and photoplethysmography imaging (PPGI)), implying potential approaches for innovative biomedical applications. Moreover, this book includes a;

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Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis

workshop aims to bring together scientists that use and develop graph-based models for the analysis of biomedical images and to encourage the;

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Electromagnetic Brain Imaging

, with specific emphasis on novel Bayesian algorithms. It helps readers to more easily understand literature in biomedical engineering and;

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An Introduction to Bayesian Biostatistics

. Rosner, Laud and Johnson make the case for Bayesian approaches by melding clear exposition on methodology with serious attention to a broad array;

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Probabilistic Graphical Models for Computer Vision.

Probabilistic Graphical Models for Computer Vision introduces probabilistic graphical models (PGMs) for computer vision problems and;

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Interrupted Time Series Analysis

provides example analyses of social, behavioral, and biomedical time series to illustrate a general strategy for building AutoRegressive Integrated;

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Bayesian Model Comparison

implementation of parameter and non-parameter-based approaches to variable selection, a survey of key results in objective Bayesian model selection;

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An Introduction to Generalized Linear Models

Continuing to emphasize numerical and graphical methods, An Introduction to Generalized Linear Models, Third Edition provides a cohesive;

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Bayesian Time Series Models

'What's going to happen next?' Time series data hold the answers, and Bayesian methods represent the cutting edge in learning what they;

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Symbolic and Quantitative Approaches to Reasoning with Uncertainty

; inconsistency; graphical models; Bayesian networks; belief functions; logic; and probabilistic graphical models for scalable data analytics. Papers come;

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Bayesian Computation with R

disciplines such as science and engineering. One reason for the dramatic growth in Bayesian modeling is the availab- ity of computational algorithms;

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An Introduction to Mathematics of Emerging Biomedical Imaging

developments in emerging biomedical imaging techniques. The main focus is on emerging multi-physics and multi-scales imaging approaches. For such;

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Graphical Models for Machine Learning and Digital Communication

book, Brendan Frey uses graphical models as an overarching framework to describe and solve problems of pattern classification, unsupervised;

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