Amazon.ca:Customer reviews: Statistical Rethinking: A Bayesian Course with Examples in R and STAN

Sort by

Filter by

295 total ratings, 30 with reviews

There was a problem filtering reviews right now. Please try again later.

Translate all reviews to English

From Canada

Amazon Customer

Awesome book for those starting out on their Bayesian journey

Reviewed in Canada 🇨🇦 on August 25, 2020

Verified Purchase

Super great intro to Bayesian statistics. The explicit use of the rethinking package as opposed to more common R packages is a bit annoying, and the allegorical explanations can be hard to follow, but there are lots of user-created resources out there to get past any of these stumbling blocks.

One person found this helpful

Helpful

Brendan Alexander

Great read

Reviewed in Canada 🇨🇦 on September 1, 2022

Verified Purchase

Great read, going to read it a second time cover to cover.
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.

Helpful

From other countries

Clive Fox

Fantastic introduction to Bayesian approaches

Reviewed in the United Kingdom 🇬🇧 on October 1, 2022

Verified Purchase

Currently working my way through the lectures on YouTube, then the written chapters and exercises. This seems an ideal way to learn for me. Having been brought up on standard frequentist approaches this is a fantastic way to get into Bayesian approaches which I've been wanting to do for some time. It's quite a long course and book so do plan on spending a couple of months to get most from it i.e. this is not a quick primer.

Alessandro

consigliato come primo libro per avvicinarsi alla statistica Bayesiana.

Reviewed in Italy 🇮🇹 on March 3, 2021

Verified Purchase

5 stelle nel suo genere, 4 nell'ambito di libri di statistica.

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.

Translate review to English

Flavio M. M. Barros

Uma das melhores introduções a inferência bayesiana

Reviewed in Brazil 🇧🇷 on December 21, 2020

Verified Purchase

Antes de falar do livro, só um background a meu respeito, eu sou bacharel em estatística e fiz mestrado na engenharia em aplicações de mineração de dados. Então eu diria que eu tenho uma formação sólida em inferência clássica (ou frequentista) e bastante intimidade com o uso do R, a ferramenta computacional usada nesse livro. Então a minha perspectiva é de alguém com experiência em estatística, mas que está explorando um outro paradigma de inferência, no caso a inferência bayesiana.

O livro vai do completamente básico em estatística até aplicações sofisticadas de métodos bayesianos de análise de dados. O nível 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 científico e pretende utilizar a inferência bayesiana para estatística aplicada em nível 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;

18 people found this helpful

Translate review to English

One of the best statistics books ever written

Reviewed in the United States 🇺🇸 on August 9, 2022

Verified Purchase

I’ve been a professional statistician for a long time, and I’ve read or tried to read a ton of books. This book covers a a lot of the tools used in day to day practice, provides clearly written useful advice, and has a practical point of view that is both mathematically sound and helps build the reader’s data intuition. One of the best statistics books I’ve ever read.

5 people found this helpful

Dr Entropy

Excellent course in Bayesian statistics

Reviewed in the United States 🇺🇸 on March 20, 2022

Verified Purchase

I must confess that I was quite hesitant to pick this book up when I first encountered the strong recommendations of experienced Bayesian practitioners. The 'cutesy' chapter titles and topics really threw me off, and as someone who actively uses Bayesian stats I was sure there would not be much for me to learn from this book.
Well, I was quite wrong. This is one of the most enjoyable technical books I have read in a long time and it really helped focus my skills and put the tools of Bayesian stats in perspective. Highly recommended to even experienced data scientists... even when he is covering ground you know well, it gives you a new way to think and communicate it to others.

8 people found this helpful

Cuong Duong

Great way to learn the fundamentals of applied Bayesian stats

Reviewed in Australia 🇦🇺 on November 3, 2020

Verified Purchase

Lots of positives about this book:
- 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).

3 people found this helpful

Mark Saroufim

The Only readable Bayesian Analysis book I own

Reviewed in the United States 🇺🇸 on June 15, 2020

Verified Purchase

Over the years I've bought many Bayesian Analysis textbooks, the reason being I knew from ML academics that working with distributions is the "true" way of doing ML instead of just point estimates like in industrial ML.

Before picking up this book I had given up on ever finding a real use-case for Bayesian ML because most of the other often recommended textbooks I owned were happy with long tedious mathematical derivations that wouldn't even bother explaining why a technique is important or how to implement it.

This book is exceptional in that it gives you the historical context behind how certain techniques were evolved and an excellent intuition for how they work. The book also comes with an R Bayesian Analysis library which also has excellent ports in Julia and Python on Github. In the case where you don't have gigantic amounts of data and where you'd like to question assumptions that you have about data, this book will teach you a way of how to think about data that is sorely lacking in any sort of Deep Learning text.

All of the algorithms in this book have stood the test of time and will continue to be relevant for the foreseeable future, thankfully this book exists to make these algorithms understandable.

43 people found this helpful

ACCGTGGTGACA...

A masterpiece of exposition

Reviewed in the United States 🇺🇸 on March 31, 2022

Verified Purchase

Combine this with Gelman's more technical Bayesian Data Analysis and you have a master class that will cover >90% of your data science challenges.

Need customer service? Click here

‹ See all details for Statistical Rethinking: A Bayesian Course with Examples in R and STAN

5 star		87%
4 star		9%
3 star		3%
2 star		1%
1 star 0% (0%)		0%

Amazon Music Stream millions of songs	Amazon Advertising Find, attract, and engage customers	Amazon Business Everything for your business	Amazon Drive Cloud storage from Amazon	Amazon Web Services Scalable Cloud Computing Services

Book Depository Books With Free Delivery Worldwide	Goodreads Book reviews & recommendations	IMDb Movies, TV & Celebrities	Amazon Photos Unlimited Photo Storage Free With Prime	Shopbop Designer Fashion Brands

Warehouse Deals Open-Box Discounts	Whole Foods Market We Believe in Real Food	Amazon Renewed Like-new products you can trust	Blink Smart Security for Every Home

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

›

Customer reviews

Customer reviews

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

There was a problem filtering reviews right now. Please try again later.

From Canada

There was a problem loading comments right now. Please try again later.

There was a problem loading comments right now. Please try again later.

From other countries

Statistical Rethinking: A Bayesian Course with Examples in R and STAN › Customer reviews

Customer reviews

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

There was a problem filtering reviews right now. Please try again later.

From Canada

There was a problem loading comments right now. Please try again later.

There was a problem loading comments right now. Please try again later.

From other countries

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

›

Customer reviews