Patients are not alike! This simple truth is often ignored in the analysis of me- cal data, since most of the time results are presented for the “average” patient. As a result, potential variability between patients is ignored when presenting, e.g., the results of a multiple linear regression model. In medicine there are more and more attempts to individualize therapy; thus, from the author’s point of view biostatis- cians should support these efforts. Therefore, one of the tasks of the statistician is to identify heterogeneity of patients and, if possible, to explain part of it with known explanatory covariates. Finite mixture models may be used to aid this purpose. This book tries to show that there are a large range of applications. They include the analysis of gene - pression data, pharmacokinetics, toxicology, and the determinants of beta-carotene plasma levels. Other examples include disease clustering, data from psychophysi- ogy, and meta-analysis of published studies. The book is intended as a resource for those interested in applying these methods.
Patients are not alike! This simple truth is often ignored in the analysis of me- cal data, since most of the time results are presented for the “average” patient. As a result, potential variability between patients is ignored when presenting, e.g., the results of a multiple linear regression model. In medicine there are more and more attempts to individualize therapy; thus, from the author’s point of view biostatis- cians should support these efforts. Therefore, one of the tasks of the statistician is to identify heterogeneity of patients and, if possible, to explain part of it with known explanatory covariates. Finite mixture models may be used to aid this purpose. This book tries to show that there are a large range of applications. They include the analysis of gene - pression data, pharmacokinetics, toxicology, and the determinants of beta-carotene plasma levels. Other examples include disease clustering, data from psychophysi- ogy, and meta-analysis of published studies. The book is intended as a resource for those interested in applying these methods.
Bayesian methods combine the evidence from the data at hand with previous quantitative knowledge to analyse practical problems in a wide range of areas. The calculations were previously complex, but it is now possible to routinely apply Bayesian methods due to advances in computing technology and the use of new sampling methods for estimating parameters. Such developments together with the availability of freeware such as WINBUGS and R have facilitated a rapid growth in the use of Bayesian methods, allowing their application in many scientific disciplines, including applied statistics, public health research, medical science, the social sciences and economics. Following the success of the first edition, this reworked and updated book provides an accessible approach to Bayesian computing and analysis, with an emphasis on the principles of prior selection, identification and the interpretation of real data sets. The second edition: Provides an integrated presentation of theory, examples, applications and computer algorithms. Discusses the role of Markov Chain Monte Carlo methods in computing and estimation. Includes a wide range of interdisciplinary applications, and a large selection of worked examples from the health and social sciences. Features a comprehensive range of methodologies and modelling techniques, and examines model fitting in practice using Bayesian principles. Provides exercises designed to help reinforce the reader’s knowledge and a supplementary website containing data sets and relevant programs. Bayesian Statistical Modelling is ideal for researchers in applied statistics, medical science, public health and the social sciences, who will benefit greatly from the examples and applications featured. The book will also appeal to graduate students of applied statistics, data analysis and Bayesian methods, and will provide a great source of reference for both researchers and students. Praise for the First Edition: “It is a remarkable achievement to have carried out such a range of analysis on such a range of data sets. I found this book comprehensive and stimulating, and was thoroughly impressed with both the depth and the range of the discussions it contains.” – ISI - Short Book Reviews “This is an excellent introductory book on Bayesian modelling techniques and data analysis” – Biometrics “The book fills an important niche in the statistical literature and should be a very valuable resource for students and professionals who are utilizing Bayesian methods.” – Journal of Mathematical Psychology
Mechanical Design Engineering Handbook, Second Edition, is a straight-talking and forward-thinking reference covering the design, specification, selection, use and integration of the machine elements that are fundamental to a wide range of engineering applications. This updated edition includes new material on tolerancing, alternative approaches to design, and robotics, as well as references to the latest ISO and US engineering regulations. Sections cover bearings, shafts, gears, seals, belts and chains, clutches and brakes, springs, fasteners, pneumatics and hydraulics, amongst other core mechanical elements. This practical handbook is an ideal shelf reference for those working in mechanical design across a variety of industries. In addition, it is also a valuable learning resource for advanced students undertaking engineering design modules and projects as part of broader mechanical, aerospace, automotive and manufacturing programs. Presents a clear, concise text that explains key component technology, with step-by-step procedures, fully worked design scenarios, component images and cross-sectional line drawings Provides essential data, equations and interactive ancillaries, including calculation spreadsheets, to inform decision-making, design evaluation and incorporation of components into overall designs Includes procedures and methods that are covered to national and international standards where appropriate New to this edition: flow-charts to help select technology; Failure Mode Effects Analysis (FMEA), product, service and system design models, Functional Analysis Diagrams (FADs), Design for Excellence (DFX), Design for MADE, and the process of remanufacture
This book is a great way to both start learning data science through the promising Julia language and to become an efficient data scientist."- Professor Charles Bouveyron, INRIA Chair in Data Science, Université Côte d’Azur, Nice, France Julia, an open-source programming language, was created to be as easy to use as languages such as R and Python while also as fast as C and Fortran. An accessible, intuitive, and highly efficient base language with speed that exceeds R and Python, makes Julia a formidable language for data science. Using well known data science methods that will motivate the reader, Data Science with Julia will get readers up to speed on key features of the Julia language and illustrate its facilities for data science and machine learning work. Features: Covers the core components of Julia as well as packages relevant to the input, manipulation and representation of data. Discusses several important topics in data science including supervised and unsupervised learning. Reviews data visualization using the Gadfly package, which was designed to emulate the very popular ggplot2 package in R. Readers will learn how to make many common plots and how to visualize model results. Presents how to optimize Julia code for performance. Will be an ideal source for people who already know R and want to learn how to use Julia (though no previous knowledge of R or any other programming language is required). The advantages of Julia for data science cannot be understated. Besides speed and ease of use, there are already over 1,900 packages available and Julia can interface (either directly or through packages) with libraries written in R, Python, Matlab, C, C++ or Fortran. The book is for senior undergraduates, beginning graduate students, or practicing data scientists who want to learn how to use Julia for data science. "This book is a great way to both start learning data science through the promising Julia language and to become an efficient data scientist." Professor Charles Bouveyron INRIA Chair in Data Science Université Côte d’Azur, Nice, France
Handbook of Evidence-Based Practice in Clinical Psychology, Volume 2 covers the evidence-based practices now identified for treating adults with a wide range of DSM disorders. Topics include fundamental issues, adult cognitive disorders, substance-related disorders, psychotic, mood, and anxiety disorders, and sexual disorders. Each chapter provides a comprehensive review of the evidence-based practice literature for each disorder and then covers several different treatment types for clinical implementation. Edited by the renowned Peter Sturmey and Michel Hersen and featuring contributions from experts in the field, this reference is ideal for academics, researchers, and libraries.
Bundled with the eBook, which will be updated regularly as new information about each virus is available, this text serves as the authoritative, up-to-date reference book for virologists, infectious disease specialists, microbiologists, and physicians, as well as medical students pursuing a career in infectious diseases.
Endothelium and Cardiovascular Diseases: Vascular Biology and Clinical Syndromes provides an in-depth examination of the role of endothelium and endothelial dysfunction in normal vascular function, and in a broad spectrum of clinical syndromes, from atherosclerosis, to cognitive disturbances and eclampsia. The endothelium is a major participant in the pathophysiology of diseases, such as atherosclerosis, diabetes and hypertension, and these entities are responsible for the largest part of cardiovascular mortality and morbidly. Over the last decade major new discoveries and concepts involving the endothelium have come to light. This important reference collects this data in an easy to reference resource. Written by known experts, and covering all aspects of endothelial function in health and disease, this reference represents an assembly of recent knowledge that is essential to both basic investigators and clinicians. Provides a complete overview of endothelial function in health and diseases, along with an assessment of new information Includes coverage of groundbreaking areas, including the artificial LDL particle, the development of a new anti-erectile dysfunction agent, a vaccine for atherosclerosis, coronary calcification associated with red wine, and the interplay of endoplasmic reticulum/oxidative stress Explores the genetic features of endothelium and the interaction between basic knowledge and clinical syndromes
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