A Student's Guide to Bayesian Statistics - by Ben Lambert (Paperback)
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<p/><br></br><p><b> About the Book </b></p></br></br>Without sacrificing technical integrity for the sake of simplicity, the author draws upon accessible, student-friendly language to provide approachable instruction perfectly aimed at statistics and Bayesian newcomers.<p/><br></br><p><b> Book Synopsis </b></p></br></br><p>Supported by a wealth of learning features, exercises, and visual elements as well as online video tutorials and interactive simulations, this book is the first student-focused introduction to Bayesian statistics.<br/> <br/> Without sacrificing technical integrity for the sake of simplicity, the author draws upon accessible, student-friendly language to provide approachable instruction perfectly aimed at statistics and Bayesian newcomers. Through a logical structure that introduces and builds upon key concepts in a gradual way and slowly acclimatizes students to using R and Stan software, the book covers: </p> <ul> An introduction to probability and Bayesian inference</li> Understanding Bayes′ rule </li> Nuts and bolts of Bayesian analytic methods</li> Computational Bayes and real-world Bayesian analysis</li> Regression analysis and hierarchical methods</li> </ul> <p>This unique guide will help students develop the statistical confidence and skills to put the Bayesian formula into practice, from the basic concepts of statistical inference to complex applications of analyses.</p><p/><br></br><p><b> Review Quotes </b></p></br></br><br><p>Written in highly accessible language, this book<em> </em>is the gateway for students to gain a deep understanding of the logic of Bayesian analysis and to apply that logic with numerous carefully selected hands-on examples. Lambert moves seamlessly from a traditional Bayesian approach (using analytic methods) that serves to solidify fundamental concepts, to a modern Bayesian approach (using computational sampling methods) that endows students with the powerful and practical powers of application. I would recommend this book and its accompanying materials to any students or researchers who wish to learn and actually <em>do</em> Bayesian modeling. </p>--Fred Oswald (7/7/2017 12:00:00 AM)<br><br>A balanced combination of theory, application and implementation of Bayesian statistics in a not very technical language. A tangible introduction to intangible concepts of Bayesian statistics for beginners.</p>--Golnaz Shahtahmassebi (7/13/2017 12:00:00 AM)<br><br>An excellent resource on Bayesian analysis accessible to students from a diverse range of statistical backgrounds and interests. Easy to follow with well documented examples to illustrate key concepts.<br/>--Bronwyn Loong (6/19/2017 12:00:00 AM)<br><br>The late, famous statistician Jimmie Savage would have taken great pleasure in this book based on his work in the 1960s on Bayesian statistics. He would have marveled at the presentations in the book of many new and strong statistical and computer analyses.<br/>--Gudmund R. Iversen (7/25/2017 12:00:00 AM)<br><br>This book offers a path to get into the field of Bayesian statistics with no previous knowledge. Building from elementary to advanced topics, including theoretic and computational aspects, and focusing on the application, it is an excellent read for newcomers to the Bayesian world.<br/>--Panagiotis Tsiamyrtzis (8/29/2017 12:00:00 AM)<br><br>When I was a grad student, Bayesian statistics was restricted to those with the mathematical fortitude to plough through source literature. Thanks to Lambert, we now have something we can give to the modern generation of nascent data scientists as a first course. Love the supporting videos, too!<br/>--Wray Buntine (6/27/2017 12:00:00 AM)<br><br>While there is increasing interest in Bayesian statistics among scholars of different social science disciplines, I always looked for a text book which is accessible to a wide range of students who do not necessarily have extended knowledge of statistics. Now, I believe that this is the first textbook of Bayesian statistics, which can also be used for social science undergraduate students. Ben Lambert begins with a general introduction to statistical inference and successfully brings the readers to more specific and practical aspects of Bayesian inference. In addition to its well-considered structure, many graphical presentations and reasonable examples contribute for a broader audience to obtain well-founded understanding of Bayesian statistics. <br/> <br/>--Susumu Shikano (8/1/2017 12:00:00 AM)<br>
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