Statistical Rethinking: A Bayesian Course with Examples in R and STAN by Richard Mcelreath

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Statistical Rethinking: A Bayesian Course with Examples in R and STAN by Richard Mcelreath

Author: ریچارد مکلره (Richard Mcelreath)

Statistical Rethinking: A Bayesian Course with Examples in R and Stan builds your knowledge of and confidence in making inferences from data. Reflecting the need for scripting in today’s model-based statistics, the book pushes you to perform step-by-step calculations that are usually automated.

This unique computational approach ensures that you understand enough of the details to make reasonable choices and interpretations in your own modeling work.

The text presents causal inference and generalized linear multilevel models from a simple Bayesian perspective that builds on information theory and maximum entropy.

The core material ranges from the basics of regression to advanced multilevel models. It also presents measurement error, missing data, and Gaussian process models for spatial and phylogenetic confounding.

The second edition emphasizes the directed acyclic graph (DAG) approach to causal inference, integrating DAGs into many examples.

The new edition also contains new material on the design of prior distributions, splines, ordered categorical predictors, social relations models, cross-validation, importance sampling, instrumental variables, and Hamiltonian Monte Carlo.

It ends with an entirely new chapter that goes beyond generalized linear modeling, showing how domain-specific scientific models can be built into statistical analyses.

[highlight color=”green”]Goodreads users review :[/highlight]

Accessible, warm, and inviting. Though with a background in numerical methods including MCMC and HMC, it is sometimes a little difficult to see much of that beautiful math being hidden away. Honestly apart from the ornate descriptions of sampling techniques, this book is a fantastic introduction to statistics in both pure and applied fields. The exercises and examples are also worth working through, something which I rarely admit. Rohit Goswami                                                                                       rated it: 5.0 from 5.0 

A true jewel in terms of content and writing style. Using Jorge Luis Borges “The Garden of Forking Paths” story as an allegory of the likelihood function is the most elegant way I’ve seen to begin a stats book. It’s possible to feel the passion and knowledge of Dr. McElreath in every sentence of the book. This is one of those books that I will take with me to my lonely island and read over and over again. Guillermo Duran                                                                                     rated it: 5.0 from 5.0 

This book is an exemplary introduction to the Bayesian thought process. It’s additionally quite good for practicing and learning R. When reading this, you will likely learn and have fun; it’s rare to find both of these (or, quite often, just one) in one text. The tone is very conversational and friendly, and Dr. McElreath doesn’t take himself too seriously. If you choose to use this book, I would strongly recommend his excellent lectures on YouTube that accompany the book. Overall, I would strongly recommend this for anyone with an interest in Bayesian statistics (which should be anyone with an interest in statistics in general) Kollin                                                                                                    rated it: 5.0 from 5.0