[HN Gopher] Bayesian Analysis with Python
___________________________________________________________________
Bayesian Analysis with Python
Author : Tomte
Score : 78 points
Date : 2024-02-09 05:44 UTC (1 days ago)
(HTM) web link (bap.com.ar)
(TXT) w3m dump (bap.com.ar)
| kingkongjaffa wrote:
| $50 (price on amazon) is a lot for an amateur self published
| author book.
|
| Thats tenured professor textbook money.
|
| Pakt publishing books can be written by anyone with even a
| surface level understanding of a topic.
| kingkongjaffa wrote:
| Heres a review from one of the other books by pakt and this
| author:
|
| If you are an inexperienced programmer or new to
| Python/Jupyter/Anaconda DO NOT BUY THIS OR ANY OTHER PACKT
| Publishing book as the code contains errors that are difficult
| to rectify. Packt Publishing DOES NOT verify code like the CRC
| Press - for instance, Statistical Rethinking by CRC press -
| i.e. Bayesian Analysis in R - has code AND excellent content
| that is helpful for both the academic and practitioner. Also,
| the Packt Publishing description/outline of probability is
| weak, at best, and is confusing to many of my students.
|
| If correcting Python code is not a big deal for you then Packt
| books are a nice intro. But why buy something that you have to
| fix before you can start working with it?
|
| This is a December 2018 update - DO NOT BUY THIS OR ANY OTHER
| PACKT PUBLISHING BOOK UNLESS YOU CAN VERIFY THERE IS AN ERRATA
| FILE TO ACCOMPANY IT. PACKT PUBLISHING DOES NOT PRODUCE
| RELIABLE TEXTS. MY STUDENTS HAVE HAD A TERRIBLE TIME WITH THIS
| BOOK - THE COMPANY HAS NOT RESPONDED TO ANY QUESTIONS/REQUESTS
| - THE UNIVERSITY WHERE I TEACH IS NOW LOOKING CLOSELY AT THE
| VALIDITY OF THESE PUBLICATIONS.
| ayhanfuat wrote:
| 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.
| UniverseHacker wrote:
| 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.
| HuShifang wrote:
| As it happens, there's a PyMC implementation of the 1st and
| 2nd editions of Statistical Rethinking here:
|
| https://github.com/pymc-devs/pymc-resources
|
| (I think the author of the book discussed above, Osvaldo
| Martin, is the primary or sole contributor for the
| Rethinking implementations, in fact -- he had a full
| implementation in his own repo
| (https://github.com/aloctavodia/Statistical-Rethinking-
| with-P...) before deprecating it in favor of the above-
| linked one.)
| perrygeo wrote:
| Agreed - that book is phenomenal at communicating the
| theory and mindset of Bayesian analysis. My favorite quote
| from the lectures - "Statistics should be subbordinate to
| Science." A very healthy emphasis on logic and proper model
| design - there will be no shoving data into black boxes!
|
| R is a good language for this but he uses a library of
| convenience functions that are not on CRAN and are
| effectively just for educational purposes. So even if you
| want to stick with R, you'll need to translate your code
| into production-ready libraries anyway. There are several
| nearly-complete translations based on other R packages as
| well as ported to Julia and Python.
| disgruntledphd2 wrote:
| Or you could just put his code into production (source: I
| worked at a super successful gaming startup that had this
| code scattered all over their repo).
|
| They had a bunch of other code problems too, but that was
| definitely the weirdest thing I saw in my (very short)
| stay there.
| fbdab103 wrote:
| You are cheating yourself if you only read the book. His
| lecture series covering the material on Youtube is
| phenomenal.
|
| I went through the book + season 1 videos, and had a glance
| at some of the season two videos. The season two has some
| visuals that made some intuition click for me.
|
| Now I see he has a 2023 playlist, which may yield even
| further improvements:
| https://www.youtube.com/playlist?list=PLDcUM9US4XdPz-
| KxHM4XH...
| UniverseHacker wrote:
| I actually took his college course the YouTube videos are
| lecture recordings of
| apwheele wrote:
| Agree about Pakt, but not about $50 self publish per se (nor
| the tenured prof remark). I am in the process of writing a code
| book, but it is compiled via quarto so at least you know the
| code works as expected.
|
| This is my favorite helpful advice for pricing, https://pubsonl
| ine.informs.org/doi/abs/10.1287/mnsc.2020.360.... Estimate max
| someone will buy, then divide by two (assuming no marginal
| cost, like e-book).
| HuShifang wrote:
| FWIW here's Aki Vehtari's approving share on his (and Gelman et
| al's) blog.
| https://statmodeling.stat.columbia.edu/2024/02/08/bayesian-a...
|
| The author was also a lead author on a CRC red-series book,
| _Bayesian Modeling and Computation in Python Learning_ ,
| published a few years ago (short review here):
|
| https://academic.oup.com/jrsssa/article/185/Supplement_2/S76...
| jononor wrote:
| I think that price point is fine. Self publishing has a lot of
| variance of course, but I would not categorically say that 50
| dollars is too high. We as potential readers just need to do
| our due diligence, checking reviews etc. A book not worth 50
| USD is not worth 30 or even 20 in my opinion - it is the wanted
| time spent reading that is the main issue.
| tussa wrote:
| I'd never buy anything from Pakt without seeing it endorsed by
| someone I trust.
|
| It's a shame because there is good stuff in their catalogue, but
| it's littered with trash.
| sheepscreek wrote:
| What's your opinion of Manning compared to Pakt?
|
| Both actively pursue potential authors. I know this because
| I've been contacted by both. Made me realize I have neither the
| will nor the patience to write a book - being an author sounds
| great on paper (pun intended).
| abecedarius wrote:
| 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.
| tussa wrote:
| I'd say black and white. I haven't seen anything as rubbish
| from Manning.
| bradyat wrote:
| Think Bayes by Allen Downey is an amazing and free course
| teaching Bayesian statistics in Python. Save yourself the $50 and
| try it first: https://allendowney.github.io/ThinkBayes2/
| mmasu wrote:
| Thanks for posting this as it seems very interesting. Did you
| go through the whole book/course? the preface claims that "You
| don't need to know calculus or linear algebra. You don't need
| any prior knowledge of statistics". I am always a bit skeptical
| when i read these claims as the texts tend ultimately to be a
| bit superficial; it would be nice to have an informed opinion
| about it before starting it out :-) thanks in advance.
| bradyat wrote:
| 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
| mmasu wrote:
| thanks!
| compumetrika wrote:
| 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.
| proamdev123 wrote:
| > 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.
| bwanab wrote:
| Totally agree. Allen Downey's work is an under appreciated
| treasure.
| richrichie wrote:
| I would recommend BDA3 and Stan.
|
| http://www.stat.columbia.edu/~gelman/book/
| argiopetech wrote:
| BDA3 is undoubtedly the authoritative source, but it's tough
| going without prior experience.
| fbdab103 wrote:
| I will second that perspective. Would steer clear as an
| introductory text.
| somethingsome wrote:
| Self plug for stan on windows in a docker dev environment:
|
| https://github.com/dbonattoj/cookiecutter-pystan
| sito42 wrote:
| numpyro is an underrated library that runs on jax which makes it
| easy to put on the gpu. They have a nice suite of examples.
|
| https://github.com/pyro-ppl/numpyro
| canyon289 wrote:
| Numpyro is fantastic, great syntax and great contributors as
| well.
|
| PyMC now supports jax and numba as well, and can use the
| numpyro samplers.
| https://www.pymc.io/projects/examples/en/latest/samplers/fas...
|
| Between all these tools we hope the modern bayesian can find
| the one that works best for them
| mvanaltvorst wrote:
| In my experience, PyMC leads to models that are orders of
| magnitudes slower than equivalent models written in JAGS.
| Profiling is also extremely tedious, and there is no section in
| the PyMC docs that touches upon model performance.
|
| I really like PyMC's API, but as soon as you move towards bigger
| datasets JAGS or Stan seem to be the only practical options.
| canyon289 wrote:
| PyMC has a pretty active community where we help people with
| questions like these. Its hard to come up with one doc as folks
| have different hardware, datasets, models etc. I hope though
| you'll the devs and community to be friendly though!
|
| https://discourse.pymc.io/t/how-to-increase-sampling-speed-w...
| ckrapu wrote:
| For models with >100 parameters, there are theoretical reasons
| for why JAGS can fail badly. It has to do with the mixing time
| of Gibbs samplers versus Hamiltonian Monte Carlo.
| hackandthink wrote:
| "Information Theory, Inference, and Learning Algorithms"
|
| This is still my favorite statistics book. MacKay is a staunch
| Bayesian, but his writing is relaxed and witty.
|
| http://www.inference.org.uk/itila/book.html
| jononor wrote:
| I recently found the Statistical Rethinking course on YouTube. I
| found it super refreshing in its very applied focus, direct
| advice and thorough pedagogy. It assumed that one has a little
| bit of stats and Bayesian thinking from before, but I found it
| accessible with just basic introduction to stats in engineering
| and ML.
|
| https://youtube.com/playlist?list=PLDcUM9US4XdPz-KxHM4XHt7uU...
| malshe wrote:
| Relatedly, I think for introductory Bayesian analysis,
| Statistical Rethinking is the best book. It uses R code but you
| will find GitHub repositories for translated Python code.
|
| In the R ecosystem, brms is currently the best (in my opinion)
| package for people who want to learn Bayesian analysis. Here is
| an online free book that translated all the original R code to
| brms code: https://bookdown.org/content/4857/
| nextos wrote:
| I prefer Gelman, Hill & Vehtari Regression and Other Stories
| (ROS). It's also free: https://avehtari.github.io/ROS-Examples.
|
| It's a great prequel to Gelman et al. Bayesian Data Analysis
| (BDA). I have a formal background in Bayesian inference, I do
| statistics for a living and I still learned many things from
| ROS. Yet, it is totally approachable for beginners.
|
| Statistical Rethinking is something you can read after ROS,
| before BDA. Personally, I think it is a great book, but it's
| not a good first book and I think it would benefit from some
| editing. For example, chapter titles are funny, but totally
| uninformative if you are browsing the book. Nonetheless, I
| still think it's a great read.
| blindstitch wrote:
| I still refer to the R notebooks I created in doing the
| question sets for RaOS. A very high quality book.
| malshe wrote:
| Oh yes, it is an excellent book too.
| canyon289 wrote:
| Disclaimer, I wrote a Bayesian Book with Osvaldo published by
| CRC. You can read it here for free!
| https://bayesiancomputationbook.com/welcome.html
|
| While I'm mostly happy with this book. Yet there's also many
| improvements we can make. We're compiling suggestions for how to
| make a second edition better so if you have any please share! We
| also recently updated the code to the newest version of PyMC.
|
| I also use(d) Bayesian stats extensively in practice at SpaceX,
| sweetgreen, now Google. It's powerful stuff, it was absolutely
| crucial in many analyses I did. It's my work at SpaceX that
| really made me appreciate Bayesian statistics.
|
| To respond to some comments below.
|
| * Osvaldo is a tenured professor, not an amateur.
|
| * PyMC can compiles to Jax and rust backends now speeding things
| up. Stan, Numpyro and other PPLs are fantastic too pick what
| works for you
|
| * There are many ways to learn Bayesian stats these days.
| Statistical Rethinking and BDA3 are great. We felt like there was
| a gap which is why we wrote another book. CRC agreed which is why
| they published it. Pick what works best for you.
|
| I did not write this Packt book with Osvaldo but I do think the
| material itself is great and it fits a certain audience.
|
| If you have questions about learning or using Bayesian stats, I'm
| happy to answer them!
| harperlee wrote:
| Thanks for your comment!
|
| Typically people have chosen one book, enjoyed it to some
| extent, and praise it, but are not well posed to comment on
| comparative advantages of each book; at most they have skimmed
| the rest.
|
| You on the other hand have made a sizable time and effort (and
| possibly money?) investment on writing a book, and you stated
| that you made that decision after identifying the gap, so I
| assume that you have a very grounded opinion on the differences
| / points of view / strengths and weaknesses of each of those
| books?
|
| It would be great if you could expand a little bit more about
| each of them.
| canyon289 wrote:
| 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.
___________________________________________________________________
(page generated 2024-02-10 23:02 UTC)