Описание: Reflecting the significant developments of the past decade, this textbook explains key physical methods in modern biology. Each method is illustrated through real-world examples, alongside background information designed for both physicists and biologists, making this an ideal resource for students in biophysics at science and medical schools.
Автор: Arora, Sanjeev Barak, Boaz Название: Computational complexity ISBN: 0521424267 ISBN-13(EAN): 9780521424264 Издательство: Cambridge Academ Рейтинг: Цена: 9029.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Describes recent achievements and classical results of computational complexity theory, including interactive proofs, PCP, derandomization, and quantum computation. It can be used as a reference, for self-study, or as a beginning graduate textbook. More than 300 exercises are included.
Due to the scale and complexity of data sets currently being collected in areas such as health, transportation, environmental science, engineering, information technology, business and finance, modern quantitative analysts are seeking improved and appropriate computational and statistical methods to explore, model and draw inferences from big data. This book aims to introduce suitable approaches for such endeavours, providing applications and case studies for the purpose of demonstration.
"Computational and Statistical Methods for Analysing Big Data with Applications" starts with an overview of the era of big data. It then goes onto explain the computational and statistical methods which have been commonly applied in the big data revolution. For each of these methods, an example is provided as a guide to its application. Five case studies are presented next, focusing on computer vision with massive training data, spatial data analysis, advanced experimental design methods for big data, big data in clinical medicine, and analysing data collected from mobile devices, respectively. The book concludes with some final thoughts and suggested areas for future research in big data.
Advanced computational and statistical methodologies for analysing big data are developed.
Experimental design methodologies are described and implemented to make the analysis of big data more computationally tractable.
Case studies are discussed to demonstrate the implementation of the developed methods.
Five high-impact areas of application are studied: computer vision, geosciences, commerce, healthcare and transportation.
Computing code/programs are provided where appropriate.
Описание: The book is addressed to statisticians working at the forefront of the statistical analysis of complex and high dimensional data and offers a wide variety of statistical models, computer intensive methods and applications: network inference from the analysis of high dimensional data;
Описание: This book contains the full papers presented at the MICCAI 2014 workshop on Computational Methods and Clinical Applications for Spine Imaging. The workshop brought together scientists and clinicians in the field of computational spine imaging. The chapters included in this book present and discuss the new advances and challenges in these fields, using several methods and techniques in order to address more efficiently different and timely applications involving signal and image acquisition, image processing and analysis, image segmentation, image registration and fusion, computer simulation, image based modeling, simulation and surgical planning, image guided robot assisted surgical and image based diagnosis.
The book also includes papers and reports from the first challenge on vertebra segmentation held at the workshop.
Автор: Hardle, Wolfgang Karl Okhrin, Yarema Okhrin, Ostap Название: Basic elements of computational statistics ISBN: 3319553356 ISBN-13(EAN): 9783319553351 Издательство: Springer Рейтинг: Цена: 9362.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This textbook on computational statistics presents tools and concepts of univariate and multivariate statistical data analysis with a strong focus on applications and implementations in the statistical software R.
Автор: Haber Название: Computational Methods in Geophysical Electromagnetics ISBN: 1611973791 ISBN-13(EAN): 9781611973792 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 10395.00 р. Наличие на складе: Нет в наличии.
Описание: Bridging the gap between theory and applications, this monograph provides a framework for the solution of electromagnetic imaging problems in geophysics. It provides a simple explanation of finite volume discretization; a full description of the basic concepts for solving inverse problems through optimization; a summary of applied electromagnetics methods; and MATLAB® code for efficient computation. The book will appeal to students and practitioners interested in computational science, data fitting, and applications to electromagnetics.
Автор: Vogel Curtis R Название: Computational Methods for Inverse Problems ISBN: 0898715504 ISBN-13(EAN): 9780898715507 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 10534.00 р. Наличие на складе: Нет в наличии.
Описание: Inverse problems arise in a number of important practical applications, ranging from biomedical imaging to seismic prospecting. This book provides the reader with a basic understanding of both the underlying mathematics and the computational methods used to solve inverse problems. It also addresses specialized topics like image reconstruction, parameter identification, total variation methods, nonnegativity constraints, and regularization parameter selection methods. Because inverse problems typically involve the estimation of certain quantities based on indirect measurements, the estimation process is often ill-posed. Regularization methods, which have been developed to deal with this ill-posedness, are carefully explained in the early chapters of Computational Methods for Inverse Problems. The book also integrates mathematical and statistical theory with applications and practical computational methods, including topics like maximum likelihood estimation and Bayesian estimation.
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