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Bayesian Ideas and Data Analysis: An Introduction for Scientists and Statisticians 5 July 2013 Bayesian Ideas and Data Analysis: An Introduction for Scientists and StatisticiansRonald Christensen, Wesley O. Johnson, 2011 | ISBN: 1439803544 | 516 pages | PDF | 6 MB Data Analysis in Forensic Science: A Bayesian Decision Perspective 13 September 2010 Data Analysis in Forensic Science: A Bayesian Decision PerspectiveWiley | 2010 | ISBN: 0470998350 | 388 pages | PDF | 12 MB This is the first text to examine the use of statistical methods in forensic science and bayesian statistics in combination. The book is split into two parts: Part One concentrates on the philosophies of statistical inference. Chapter One examines the differences between the frequentist, the likelihood and the Bayesian perspectives, before Chapter Two explores the Bayesian decision–theoretic perspective further, and looks at the benefits it carries. Bayesian Analysis of Failure Time Data Using P-Splines 9 March 2015 Bayesian Analysis of Failure Time Data Using P-SplinesSpringer | Anatomy, Bioinformatics, Molecular Biology | Dec 27 2014 | ISBN-10: 3658083921 | 110 pages | pdf | 2.95 mb Matthias Kaeding discusses Bayesian methods for analyzing discrete and continuous failure times where the effect of time and/or covariates is modeled via P-splines and additional basic function expansions, allowing the replacement of linear effects by more general functions. Bayesian Analysis of Gene Expression Data 24 December 2014 Bani K. Mallick, David Gold, "Bayesian Analysis of Gene Expression Data" 2009 | ISBN-10: 0470517662 | 252 pages | PDF | 4 MB Case Studies in Bayesian Statistical Modelling and Analysis 20 August 2013 Case Studies in Bayesian Statistical Modelling and Analysis by Clair L. Alston, Kerrie L. Mengersen and Anthony N. PettittEnglish | 2012 | ISBN: 1119941822 | 598 pages | PDF | 11,8 MB Provides an accessible foundation to Bayesian analysis using real world models This book aims to present an introduction to Bayesian modelling and computation, by considering real case studies drawn from diverse fields spanning ecology, health, genetics and finance. The Handbook of Applied Bayesian Analysis 27 September 2012 The Handbook of Applied Bayesian AnalysisEnglish | 2010 | ISBN: 0199548900 | 896 pages | PDF | 10,2 MB Bayesian Adaptive Methods for Clinical Trials 9 July 2013 Bayesian Adaptive Methods for Clinical Trials2010 | ISBN: 1439825483 | PDF | 311 pages | 4 MB Already popular in the analysis of medical device trials, adaptive Bayesian designs are increasingly being used in drug development for a wide variety of diseases and conditions, from Alzheimer’s disease and multiple sclerosis to obesity, diabetes, hepatitis C, and HIV. Written by leading pioneers of Bayesian clinical trial designs, Bayesian Adaptive Methods for Clinical Trials explores the growing role of Bayesian thinking in the rapidly changing world of clinical trial analysis. System and Bayesian Reliability: Essays in Honor of Professor Richard E. Barlow 4 October 2011 System and Bayesian Reliability: Essays in Honor of Professor Richard E. Barlow 2002 | 409 | ISBN: 9810248652 | PDF | 15 Mb This volume is a collection of articles on reliability systems and Bayesian reliability analysis. Written by reputable researchers, the articles are self-contained and are linked with literature reviews and new research ideas. The book is dedicated to Emeritus Professor Richard E. Barlow, who is well known for his pioneering research on reliability theory and Bayesian reliability analysis. ... Bayesian Analysis for the Social Sciences 28 December 2014 Simon Jackman, "Bayesian Analysis for the Social Sciences" 2009 | ISBN-10: 0470011548 | 598 pages | PDF | 6 MB Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis, 2nd edition (PDF) 30 September 2013 Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis, 2nd edition2013 | ISBN-10: 1461451035 | 412 pages | PDF | 3 MB Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis, Second Edition, provides a comprehensive guide for practitioners who wish to understand, construct, and analyze intelligent systems for decision support based on probabilistic networks. |