
Download the free Kindle app and start reading Kindle books instantly on your smartphone, tablet or computer – no Kindle device required. Learn more
Read instantly on your browser with Kindle for Web.
Using your mobile phone camera, scan the code below and download the Kindle app.


Statistical Rethinking: A Bayesian Course with Examples in R and STAN Hardcover – March 16 2020
Amazon Price | New from | Used from |
Kindle Edition
"Please retry" | — | — |
- Kindle Edition
$9.99 Read with Our Free App - Hardcover
$109.56
Enhance your purchase
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.
Features
- Integrates working code into the main text
- Illustrates concepts through worked data analysis examples
- Emphasizes understanding assumptions and how assumptions are reflected in code
- Offers more detailed explanations of the mathematics in optional sections
- Presents examples of using the dagitty R package to analyze causal graphs
- Provides the rethinking R package on the author's website and on GitHub
- ISBN-10036713991X
- ISBN-13978-0367139919
- Edition2
- PublisherChapman and Hall/CRC
- Publication dateMarch 16 2020
- LanguageEnglish
- Dimensions18.8 x 2.29 x 25.65 cm
- Print length594 pages
Frequently bought together
- +
- +
Customers who viewed this item also viewed
Product description
Review
"The first edition (and this second edition) of *Statistical Rethinking* beautifully outlines the key steps in the statistical analysis cycle, starting from formulating the research question. I find that many statistics textbooks omit the issue of problem formulation and either jump into data acquisition or further into analysis after the fact. McElreath has created a fantastic text for students of applied statistics to not only learn about the Bayesian paradigm, but also to gain a deep appreciation for the statistical thought process. I also found that many students appreciated McElreath’s engaging writing style and humor, and personally found the infusion of humor quite refreshing."
- Adam Loy, Carleton College
"(The chapter) ‘Generalized Linear Madness’ represents another great chapter of an even better edition of an already awesome textbook."
- Benjamin K. Goodrich, Columbia University
"(Chapter 16) is a worthy concluding chapter to a masterful book. Eminently readable and enjoyable. Brimful of small thought-provoking bits which may inspire deeper studies, but first and foremost a window on the trial and error process involved in building a statistical model or rather, indeed, any scientific theory."
- Josep Fortiana Gregori, University of Barcelona
"I do regard the manuscript as technically correct, clearly written, and at an appropriate level of difficulty. The technical approaches and the R codes of the book are perfect for our students. They can learn concepts of Bayesian models, data analysis, and model validation methods through using the R codes. The codes help students to have better understanding of the models and data analysis process."
- Nguyet Nguyen, Youngstown State University
"As a textbook it successfully brings the statistician’s toolbox to a wider audience with an accessible style and good humour. It should be recommended to statistics students, both old and new."
- Nathan Green, Journal of the Royal Statistical Society, 2021, https://doi.org/10.1111/rssa.12755
"In conclusion, Statistical Rethinking frames usual methods and tools taught in graduate statistical courses into a different way to encourage the reader to understand the details and appreciate the underlying assumptions. The accompanying R package offers example codes for some interesting problems that are not available in standard library or other popular packages. This book can be used as a supplement to a graduate course or it can be used by practitioners wanting to brush up their knowledge with better understanding of statistical techniques."
- Abhirup Mallik in Technometrics, August 2021
"The first edition (and this second edition) of *Statistical Rethinking* beautifully outlines the key steps in the statistical analysis cycle, starting from formulating the research question. I find that many statistics textbooks omit the issue of problem formulation and either jump into data acquisition or further into analysis after the fact. McElreath has created a fantastic text for students of applied statistics to not only learn about the Bayesian paradigm, but also to gain a deep appreciation for the statistical thought process. I also found that many students appreciated McElreath’s engaging writing style and humor, and personally found the infusion of humor quite refreshing."
~Adam Loy, Carleton College
"(The chapter) ‘Generalized Linear Madness’ represents another great chapter of an even better edition of an already awesome textbook."
~Benjamin K. Goodrich, Columbia University
"(Chapter 16) is a worthy concluding chapter to a masterful book. Eminently readable and enjoyable. Brimful of small thought-provoking bits which may inspire deeper studies, but first and foremost a window on the trial and error process involved in building a statistical model or rather, indeed, any scientific theory."
~Josep Fortiana Gregori, University of Barcelona
"I do regard the manuscript as technically correct, clearly written, and at an appropriate level of difficulty. The technical approaches and the R codes of the book are perfect for our students. They can learn concepts of Bayesian models, data analysis, and model validation methods through using the R codes. The codes help students to have better understanding of the models and data analysis process."
~Nguyet Nguyen, Youngstown State University
"In conclusion, Statistical Rethinking frames usual methods and tools taught in graduate statistical courses into a different way to encourage the reader to understand the details and appreciate the underlying assumptions. The accompanying R package offers example codes for some interesting problems that are not available in standard library or other popular packages. This book can be used as a supplement to a graduate course or it can be used by practitioners wanting to brush up their knowledge with better understanding of statistical techniques."
~Abhirup Mallik in Technometrics, August 2021
"As a textbook it successfully brings the statistician’s toolbox to a wider audience with an accessible style and good humour. It should be recommended to statistics students, both old and new."
~ Nathan Green, Journal of the Royal Statistical Society, 2021
About the Author
Richard McElreath studies human evolutionary ecology and is a Director at the Max Planck Institute for Evolutionary Anthropology in Leipzig, Germany. He has published extensively on the mathematical theory and statistical analysis of social behavior, including his first book (with Robert Boyd), Mathematical Models of Social Evolution.
Product details
- Publisher : Chapman and Hall/CRC; 2 edition (March 16 2020)
- Language : English
- Hardcover : 594 pages
- ISBN-10 : 036713991X
- ISBN-13 : 978-0367139919
- Item weight : 1.42 kg
- Dimensions : 18.8 x 2.29 x 25.65 cm
- Best Sellers Rank: #38,908 in Books (See Top 100 in Books)
- #3 in Geochemistry
- #3 in Geochemistry in Professional Science
- #10 in Software Engineering Methodology
- Customer Reviews:
About the author

Discover more of the author’s books, see similar authors, read author blogs and more
Customer reviews
-
Top reviews
Top reviews from Canada
There was a problem filtering reviews right now. Please try again later.
Make sure to watch the youtube videos too.
He actually codes a grid search in one of the initial chapters that makes it very clear how the posterior is calculated.
I wouldn't mind more of a discussion on coding in Stan, or using custom covariance matrices, but that may just be outside the scope of the book.
Put "Causality" (Pearl) on your reading list as well.
Top reviews from other countries


The book is basic enough to be understandable to non-mathematicians/ non-statisticians but not so basic that it's boring/ redundant. The R code examples are great for learning how to use R.

O livro vai do completamente básico em estatÃstica até aplicações sofisticadas de métodos bayesianos de análise de dados. O nÃvel matemático exigido é relativamente baixo, e inclusive o autor deixa claro que o livro não demanda um conhecimento profundo de cálculo ou álgebra linear, e o livro faz muito uso do método computacional para o ensino, isto é, são apresentados os conceitos e o leitor tem a oportunidade de implementar os métodos e discutir os resultados ao longo do texto. Mas não se enganem, o livro é direcionado a alguém que conhece e entende o método cientÃfico e pretende utilizar a inferência bayesiana para estatÃstica aplicada em nÃvel de pós-graduação. Não é uma introdução superficial apesar de que eu acredito que um graduando, bastante motivado, poderia aproveitar bem esse livro. Mas, por outro lado, é um livro muito gostoso de ler e aprender e o autor apresenta a inferência bayesiana sob uma perspectiva nova na minha opinião. Acho que como introdução ao assunto não tem nenhum livro tão bom quanto esse no mercado.
Alguns destaques que esse livro teve para mim foram:
1) mostrar como a inferência bayesiana é basicamente um processo de contagem;
2) o pacote rethinking do R que é muito útil para usar com o livro mas também para implementar as próprias análises no futuro;
3) os DAGs (direct acyclic graph) e a inferência causal; nunca tinha visto isso e foi um divisor de águas para mim;
4) a discussão sobre entropia e as distribuições de probabilidade;
5) as discussões sobre MCMC, e especialmente sobre o HMC (monte carlo hamiltoniano). Nas aulas online as simulações que mostram a diferença dos algoritmos de Metropolis e do Gibbs para o HMC foram reveladoras para mim;
6) o fato de ter um curso online do livro no Youtube, onde você pode ler o livro e assistir as aulas junto, o que foi uma tremenda experiência para mim;

- Accompanying lectures by the author which are available online for free on his YouTube channel
- Author tries to make Bayesian stats as intuitive as possible, and most explanations are by examples and code rather than written math.
- Places heavy emphasis on the use of Bayesian stats for inference rather than predictive modelling (but does explain the importance of good model fit, etc. as well).
- Explains how to set good priors, with examples, which is usually missing in a lot of other instructive material on Bayesian modelling.
Some things to note that might be issues depending on your specific needs:
- Examples are pretty reliant on the rethinking package, instead of pure Stan or rstan. This is a small issue though since there are reference manuals online for how to use those tools (the book is more about teaching the Bayesian way of thinking and causal inference rather than a specific tool).
- There is a focus on the social sciences so there's little application to 'bigger data' domains where distributions are a little different and data size can be an issue for Bayesian inference (e.g. Tech). Book will provide good fundamentals for extending to this kind of domain though.
- Probably not for more intermediate or advanced users of Bayesian stats (e.g. you've already built a few models end to end).

Statistical Rethinking di Richard McElreath e` un libro di statistica con poca matematica e molta parte discorsiva, i 16 capitoli potrebbero anche essere 16 post (molto) lunghi di un blog di statistica Bayesiana.
L'autore cerca di dare piu` una comprensione intuitiva dei vari concetti piuttosto che seguire un approccio basato su definizioni rigorose e concise quindi chi e` abituato ad una trattazione matematica rimarra` probabilmente deluso.
Ma l'intento e` proprio quello di evitare eccessivi o inutili formalismi e concentrarsi su esempi (con codice in R presente nel libro e scaricabile) svolti e ben descritti.
Da qui la mia valutazione di 5 stelle per il genere "poca matematica" mentre se dovessi valutarlo come elemento piu` generale dell'insieme dei libri di statistica darei solo 4 stelle perche' sono convinto che il giusto formalismo arricchirebbe ulteriormente l'approccio dell'autore (che forse teme di alienarsi un pubblico che secondo certi stereotipi e` refrattario alla matematica).
Lo stile alle volte e`, almeno per me, piacevolmente provocatorio, come puo` leggersi in questi due brani:
In the sciences, there is sometimes a culture of anxiety surrounding statistical inference.
It used to be that researchers couldn’t easily construct and study their own custom models, because they had to rely upon statisticians to properly study the models first.
This led to concerns about unconventional models, concerns about breaking the laws of statistics.
But statistical computing is much more capable now.
Now you can imagine your own generative process, simulate data from it, write the model, and verify that it recovers the true parameter values.
You don’t have to wait for a mathematician to legalize the model you need.
Probability theory is not difficult mathematically. It is just counting.
But it is hard to interpret and apply.
Doing so often seems to require some cleverness, and authors have an incentive to solve problems in clever ways, just to show off.
But we don’t need that cleverness, if we ruthlessly apply conditional probability.
in conclusione: consigliato come primo libro per avvicinarsi alla statistica Bayesiana.