Book Bayesian Meta-Analysis: A Practical Introduction PDF Download - Robert Grant, Gian Luca Di Tanna
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Bayesian Meta-Analysis: A Practical Introduction
Robert Grant, Gian Luca Di Tanna
Page: 328
Format: pdf, ePub, mobi, fb2
ISBN: 9781032451893
Publisher: CRC Press
Download or Read Online Bayesian Meta-Analysis: A Practical Introduction Free Book (PDF ePub Mobi) by Robert Grant, Gian Luca Di Tanna
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"…this book is extremely timely…not just a technical exposition, but provides practical guidance about using different software platforms, as well as valuable advice about extracting summary statistics, eliciting prior information, communicating results, visualisation, and many other issues…reflects years of thoughtful experience, and should be of huge value to anyone faced with pooling studies into a coherent whole."
~From the Foreword by Professor Sir David Spiegelhalter
Meta-analysis is the statistical combination of previously conducted studies, often from summary statistics but sometimes with individual participant data. It is widespread in life sciences and is gaining popularity in economics and beyond. In many real-life meta-analyses, challenges in the source information, such as unreported statistics or biases, can be incorporated using Bayesian methods. Bayesian Meta-Analysis: A Practical Introduction provides an approachable introduction for researchers who are new to Bayes, meta-analysis, or both. There is an emphasis on hands-on learning using a variety of software packages. Key Features Introductory chapters assume no prior experience or mathematical training, and are aimed at non-statistical researchers Examples of basic meta-analyses in seven different software alternatives: BUGS, JAGS, Stan, bayesmeta, brms, Stata, and JASP Practical advice on extracting information from studies, eliciting expert opinions, managing project decisions, and writing up findings Discussion of specific problems, including publication bias, unreported statistics, and a mixture of study designs, with code examples Accompanying online blog and forum, with all code and data from the book, plus more translations to different software This book aims to bridge the gap between the researcher who wants to carry out tailored meta-analysis and the techniques they need, which have previously been available only in mathematically or computationally demanding publications.
The BUGS Book: A Practical Introduction to Bayesian Analysis
It also features a large number of worked examples, a wide range of applications from various disciplines, and numerous detailed exercises in every chapter.
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Hardcover Book (2025) $ 166.99. Bayesian Meta-Analysis: A Practical Introduction. Robert Grant. Provides an approachable introduction for researchers who are .
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Through this book, the reader will be able to develop a practical ability to apply Bayesian modeling within their own field. The book begins with an informal .
Bayesian Meta-Analysis: A Practical Introduction (Paperback)
$74.99 · Introductory chapters assume no prior experience or mathematical training, and are aimed at non-statistical researchers · Examples of basic meta-analyses .
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@book{prince2023understanding, author . Maximum likelihood; Maximum a posteriori; The Bayesian approach; Example: 1D linear regression; Practical concerns.
Bayesian Meta-Analysis: A Practical Introduction - Robert Grant
Provides an approachable introduction for researchers who are new to Bayes, meta-analysis, or both. There is an emphasis on hands-on learning using a .
What is the best book to learn Meta-Analysis that I can recommend .
It depends, if you want a theoretical basic book , I think “Introduction to Meta-Analysis" by Borenstein and others would be of great value.
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The BUGS Book : A Practical Introduction to Bayesian Analysis - Free PDF Download - Chris, Jackson,. - 393 Pages - Year: 2012 - Read .
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A clear and thorough introduction to meta-analysis, the process of synthesizing data from a series of separate studies.
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This book provides a very clear overview of meta-analysis – what it is, why we use it, how to perform and interpret a meta-analysis, and how to avoid common .