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Computational Statistics
This new edition continues to serve as a comprehensive guide to modern and classical methods of statistical computing. The book is comprised of four main parts spanning the field: Optimization Integration and Simulation Bootstrapping Density Estimation and Smoothing Within these sections,each chapter includes a comprehensive introduction and step-by-step implementation summaries to accompany the explanations of key methods. The new edition includes updated coverage and existing topics as well as new topics such as adaptive MCMC and bootstrapping for correlated data. The book website now includes comprehensive R code for the entire book. There are extensive exercises, real examples, and helpful insights about how to use the methods in practice.
A Wavelet Tour of Signal Processing
Mallat's book is the undisputed reference in this field - it is the only one that covers the essential material in such breadth and depth. - Laurent Demanet, Stanford UniversityThe new edition of this classic book gives all the major concepts, techniques and applications of sparse representation, reflecting the key role the subject plays in today's signal processing. The book clearly presents the standard representations with Fourier, wavelet and time-frequency transforms, and the construction of orthogonal bases with fast algorithms. The central concept of sparsity is explained and applied to signal compression, noise reduction, and inverse problems, while coverage is given to sparse repres...
Clinical Handbook for the Management of Mood Disorders
This Clinical Handbook for the Management of Mood Disorders will equip clinicians with the knowledge to refine their diagnostic skills and implement treatment plans for mood disorders based on the most up-to-date evidence on interventions that work. Covering the widest range of treatments and techniques, it provides clear guidance for the management of all types and subtypes of both minor and major depression. Chapters cover the latest and most innovative treatments, including use of ketamine, deep brain stimulation and transcranial magnetic stimulation, effective integration of pharmacological and psychotherapeutic approaches, as well as providing a thought-provoking look at the future research agenda and the potential for reliable biomarkers. This is the most comprehensive review of depression available today. Written and edited by leading experts mostly from Columbia University, this is an essential resource for anyone involved in the care and treatment of patients with mood disorders.
The Wondrous Story of Anesthesia
Edited and written by an international "who's who" of more than 100 authors, including anesthesiologists, nurse anesthetists, bench scientists, a surgeon, and representatives of industry, this text provides a comprehensive history of anesthesia, unique in its focus on the people and events that shaped the specialty around the world, particularly during the past 70 years when anesthesia emerged from empiricism and developed into a science-based practice.
The Elements of Statistical Learning
During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, wi...
Machine Learning in Python
Learn a simpler and more effective way to analyze data and predict outcomes with Python Machine Learning in Python shows you how to successfully analyze data using only two core machine learning algorithms, and how to apply them using Python. By focusing on two algorithm families that effectively predict outcomes, this book is able to provide full descriptions of the mechanisms at work, and the examples that illustrate the machinery with specific, hackable code. The algorithms are explained in simple terms with no complex math and applied using Python, with guidance on algorithm selection, data preparation, and using the trained models in practice. You will learn a core set of Python program...
Probabilistic Machine Learning
A detailed and up-to-date introduction to machine learning, presented through the unifying lens of probabilistic modeling and Bayesian decision theory. This book offers a detailed and up-to-date introduction to machine learning (including deep learning) through the unifying lens of probabilistic modeling and Bayesian decision theory. The book covers mathematical background (including linear algebra and optimization), basic supervised learning (including linear and logistic regression and deep neural networks), as well as more advanced topics (including transfer learning and unsupervised learning). End-of-chapter exercises allow students to apply what they have learned, and an appendix covers...
Image Processing and Pattern Recognition
Image Processing and Pattern Recognition covers major applications in the field, including optical character recognition, speech classification, medical imaging, paper currency recognition, classification reliability techniques, and sensor technology. The text emphasizes algorithms and architectures for achieving practical and effective systems, and presents many examples. Practitioners, researchers, and students in computer science, electrical engineering, andradiology, as well as those working at financial institutions, will value this unique and authoritative reference to diverse applications methodologies.Coverage includes: - Optical character recognition - Speech classification - Medical imaging - Paper currency recognition - Classification reliability techniques - Sensor technology Algorithms and architectures for achieving practical and effective systems are emphasized, with many examples illustrating the text. Practitioners, researchers, and students in computer science, electrical engineering, and radiology, as wellk as those working at financial institutions, will find this volume a unique and comprehensive reference source for this diverse applications area.
Spectroscopy of Solid-State Laser-Type Materials
This book presents an account of the course "Spectroscopy of Solid-State Laser-Type Materials" held in Erice, Italy, from June 16 to 30, 1985. This meeting was organized by the International School of Atomic and Molecular Spectroscopy of the "Ettore Majorana" Centre for Scientific Culture. The objective of the course was to present and examine the recent advances in spectroscopy and theoretical modelling relevant to the interpretation of luminescence and laser phenomena in several classes of solid-state materials. The available solid-state matrices (e.g. halides, oxides, glasses, semiconductors) and the full range of possible activators (transition ions, rare earth ions, post-transition ions...
