Bayesian Analysis with Python

Yeah I have completed it, and I really like his teaching style. I’d say it might be a little tough if you have absolutely no knowledge of statistics. But as someone who wanted to do a deeper dive into Bayesian stats specifically I found it a good resource. The reason I ended up doing it was precisely because I felt that the section on bayes in most stats courses is too superficial

I worked through large chunks of the book a long time ago (perhaps an earlier version). I agree with the other responder-- if you've had zero stats it may be more difficult, but the author does an excellent job of discretizing almost everything, which means that integrals and derivatives are replaced with adding and subtracting. For this book I think the most advanced math you need is multiplication and division. It's a clever trick that relies on the fact that computers are really good at multiplying 10,000 things by 10,000 other things and adding them up (which is what discretization implies here).

Before computers that was much harder to do so you needed clever math tricks to "do it all at once," which gets you to all the analytical methods -- calculus, etc. Still all very useful! Just hard to teach to a broad audience. Downey really leans into the technical advantage to computers provide and thus can cover a lot of material before getting into complicated math.

> the preface claims that “You don’t need to know calculus or linear algebra. You don’t need any prior knowledge of statistics”.

Downey makes this statement because his premise is that if you know Python, you can use that knowledge to learn the concepts without the pure math approach.

For example, he will do iteration to calculate integrals rather than teaching integration. And he will plot statistical distributions using a Python library to explore and teach the statistical concepts.

He also has a book called “Think Statistics” that is excellent in this regard.

Manning seems fine to me. I've read a few books from them, from quite good to blah.

I've read one book from Packt that was quite good; they've also published literal plagiarism (which they withdrew after the original author, a friend, tweeted about it -- this wouldn't have come to my attention otherwise). Can't recall seeing another that interested me, flipping through the pages.

I'd say black and white. I haven't seen anything as rubbish from Manning.

I still refer to the R notebooks I created in doing the question sets for RaOS. A very high quality book.

Oh yes, it is an excellent book too.

Thank you for asking. I find this stuff super interesting. I write about it on my blog

https://ravinkumar.com/

and here's a video for the most common question I get about bayes vs frequentist

https://www.youtube.com/watch?v=foSPfzYs4yY

For recommendations here's my suggestions split by persona

* I want a PHD in statistics or to write novel research - Go read BDA3 cover to cover. Gelman etal are amazing, and amazingly brilliant. The book is dense though, even after years of experience I need to now read chapters 2 or 3 times and write out proofs

* I want have a smooth on ramp into Bayesian stats with lots of code and beautiful writing. I also like video lectures and undergraduate college courses - Statistical Rethinking. As noted below Richard does a wonderful job explaining these concepts with metaphors like golems, interspersing it with this experience as an anthropologist, and using his teaching experience to write well structured introduction into Bayesian stats. This book does assume you understand basic statistics and probability theory.

* I want a great comparison of Bayesian vs Frequentist stats- This book covers both topics well, compares them fairly, and has all the proofs to back things up https://www.routledge.com/Understanding-Advanced-Statistical...

* I'm a programmer type person that likes hands on "build from scratch" using code - Allen Downey's think bayes builds up bayes theorem from Numpy arrays. He's also a brilliant instructors

* I want to read about the history and people and politics - Bernoulli's fallacy, The Theory That Would Not Die:, and Probably Overthinking it are all "non mathy" great armchair readings https://cup.columbia.edu/book/bernoullis-fallacy/97802311999... https://www.amazon.com/Theory-That-Would-Not-Die/dp/03001882... https://www.allendowney.com/blog/

* Im an applied practitioner that is focusing more on my specific problem and I need to use the latest PPLs and code to get it done - This is my book. I had to make estimations in SpaceX supply chain with some quick deadlines and I didn't have time to take an undergrad course. I also needed my code to be robust, testable, and scalable. I didn't find that other books provided this so that's why I wrote this with CRC and Osvaldo. Osvaldo and I are heavy contributors to PyMC, ArviZ, Preliz and other libraries so naturally we take a code first approach. https://bayesiancomputationbook.com/welcome.html

* (Shameless self promotion) Im a professional, I need to learn fast, and my company will pay for training - For this specific niche me and other Bayesian colleagues created an online course designed specifically for professionals. Yes it's expensive so let me plainly state no one at any point needs to spend any money to learn Bayesian stats. That being said hundreds of people have purchased this course and the feedback we've gotten on this course has been quite positive. So I want to underscore before Hacker news rips me apart. No one is being forced to buy this, if you want this style course here it is, if you don't there's many many ways to learn Bayesian stats. https://www.intuitivebayes.com/

You can sign up for the Gaussian Process course for free if you'd like https://www.intuitivebayes.com/gaussian-processes

As you can tell I'm very fascinated by generative modeling. I find it fun to think about. I also frankly find it lucrative. It's helped me job hop across some pretty cool companies and get through the Google interview. The combination of strong programming skillset with applied generative mathematics is only heating up so I feel lucky to have chanced upon it, and also thankful that many others before me put great code and reading material out there so I could learn myself.

It is not just code. Content in general is horrible, too. You put together a few badly written blog posts and it becomes a Packt published book.

I want to second the recommendation for Statistical Rethinking from that review, it’s one of the best books ever written on computational Bayesian inference.

However, it is R based, and therefore arguably not an alternative to the book we are discussing here.

I will second that perspective. Would steer clear as an introductory text.

It is a challenging book. But it is an introductory level material on Bayesian statistics. One can teach an undergrad course from the book. IMO, doing computation along the way makes the process enjoyable. Stan / julia-turing / pymcmc all good tools.