#11595
Mentioned in 3 episodes

Statistical Rethinking

Book • 2015
Statistical Rethinking is a comprehensive guide to statistical modeling and causal inference, emphasizing Bayesian methods.

It introduces modern computational tools for data analysis, focusing on understanding the underlying principles rather than rote memorization.

The book covers a wide range of topics, including linear models, multilevel models, and causal inference techniques.

It encourages readers to think critically about model assumptions and to use simulation to validate their analyses.

Statistical Rethinking is designed to equip students and researchers with the skills to tackle complex real-world problems.

The book promotes a hands-on approach to learning, emphasizing the importance of understanding both the mathematical foundations and the practical applications of statistical modeling.

Mentioned by

Mentioned in 3 episodes

Recommended by David Moreau to
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Christoph Bamberg
, who started reading the first edition, watched the video lectures, and then got the second edition.
14 snips
#143 Transforming Nutrition Science with Bayesian Methods, with Christoph Bamberg
Mentioned by
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Gabriel Stechschulte
as one of the books that introduced him to Bayesian statistics.
#142 Bayesian Trees & Deep Learning for Optimization & Big Data, with Gabriel Stechschulte
Mentioned by
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Alex Andorra
as emphasizing Judea Pearl's framework in the second edition.
#34 Multilevel Regression, Post-stratification & Missing Data, with Lauren Kennedy
Recommended by David Moreau, mentioned by
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Christoph Bamberg
as his foundation for Bayesian statistics.
BITESIZE | Are Bayesian Models the Missing Ingredient in Nutrition Research?

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