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Bayesian Ideas and Data Analysis: An Introduction for Scientists and Statisticians

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Foundational Bayesian statistics for scientific applications.

If you're looking to understand the Bayesian perspective on statistics, particularly in scientific research, this book could be a goldmine for you. It takes complex ideas and makes them accessible, offering practical guidance on applying Bayesian methods to real data analysis. You'll appreciate the real-world examples that can crystalize abstract concepts.

Note: While we do our best to ensure the accuracy of cover images, ISBNs may at times be reused for different editions of the same title which may hence appear as a different cover.
New

Bayesian Ideas and Data Analysis: An Introduction for Scientists and Statisticians

Regular price $48.90
Unit price
per
ISBN: 9781439803547
Publisher: CRC Press
Date of Publication: 2010-07-02
Format: Hardcover
Related Collections: Personal Development, Science, Economics
Related Topics: Popular Science, Mathematics
Goodreads rating: 3.89
(rated by 9 readers)

Description

Emphasizing the use of WinBUGS and R to analyze real data, Bayesian Ideas and Data Analysis presents statistical tools to address scientific questions. It highlights foundational issues in statistics, the importance of making accurate predictions, and the need for scientists and statisticians to collaborate in analyzing data. The WinBUGS code provided offers a convenient platform to model and analyze a wide range of data. The first five chapters of the book contain core material that spans basic Bayesian ideas, calculations, and inference, including modeling one and two sample data from traditional sampling models. The text then covers Monte Carlo methods, such as Markov chain Monte Carlo (MCMC) simulation. After discussing linear structures in regression, it presents binomial regression, normal regression, analysis of variance, and Poisson regression, before extending these methods to handle correlated data. The authors also examine survival analysis and binary diagnostic testing. A complementary chapter on diagnostic testing for continuous outcomes is available on the book’s website. The last chapter on nonparametric inference explores density estimation and flexible regression modeling of mean functions. The appropriate statistical analysis of data involves a collaborative effort between scientists and statisticians. Exemplifying this approach, Bayesian Ideas and Data Analysis focuses on the necessary tools and concepts for modeling and analyzing scientific data. Data sets and codes are provided on a supplemental.
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Foundational Bayesian statistics for scientific applications.

If you're looking to understand the Bayesian perspective on statistics, particularly in scientific research, this book could be a goldmine for you. It takes complex ideas and makes them accessible, offering practical guidance on applying Bayesian methods to real data analysis. You'll appreciate the real-world examples that can crystalize abstract concepts.

Note: While we do our best to ensure the accuracy of cover images, ISBNs may at times be reused for different editions of the same title which may hence appear as a different cover.