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Bayesian Data Analysis, Third edition (2013) [pdf]

sites.stat.columbia.edu

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Re: Bayesian Data Analysis, Third edition (2013) [pdf]

#61

Post by oogway8020 »

pyyxbkshed wrote: post #20
kianN wrote: post #13

This is my favorite book on statistics. Full stop. The author Andrew Gelman created a whole new branch of Bayesian statistics with both his theoretical work on hierarchical modeling while also publishing Stan to enable practical applications of hierarchical models. It took me about a year to work through this book on the side (including the exercises) and it provided the foundation for years of fruitful research into…

What is a book / course on statistics that I can go through before this so that I can understand this?

Here is one path to learn Bayesian starting from basics, assuming modern R path with tidyverse (recommended):

First learn some basic probability theory: Peter K. Dunn (2024). The theory of distributions. https://bookdown.org/pkaldunn/DistTheory

Then frequentist statistics: Chester Ismay, Albert Y. Kim, and Arturo Valdivia - https://moderndive.com/v2/ Mine Çetinkaya-Rundel and Johanna Hardin - https://openintrostat.github.io/ims/

Finally Bayesian: Johnson, Ott, Dogucu - https://www.bayesrulesbook.com/ This is a great book, it will teach you everything from very basics to advanced hierachical bayesian modeling and all that by using reproducible code and stan/rstanarm

Once you master this, next level may be using brms and Solomon Kurz has done full Regression and Other Stories Book using tidyerse/brms. His knowledge of tidyverse and brms is impressive and demonstrated in his code. https://github.com/ASKurz/Working-through-Regression-and-oth...

Re: Bayesian Data Analysis, Third edition (2013) [pdf]

#62

Post by srean »

twiecki wrote: post #49
pyyxbkshed wrote: post #20

Earlier quoted context omitted.

What is a book / course on statistics that I can go through before this so that I can understand this?

There is a collection of curated resources here: https://www.pymc.io/projects/docs/en/stable/learn.html

I would really love to have the story of PyMC told, especially it's technical evolution, how it was implemented first and how it changed over the years.

Re: Bayesian Data Analysis, Third edition (2013) [pdf]

#64

Post by kianN »

djmips wrote: post #54
kianN wrote: post #13

This is my favorite book on statistics. Full stop. The author Andrew Gelman created a whole new branch of Bayesian statistics with both his theoretical work on hierarchical modeling while also publishing Stan to enable practical applications of hierarchical models. It took me about a year to work through this book on the side (including the exercises) and it provided the foundation for years of fruitful research into…

Can you explain to me in simple terms how your fruitful research benefited you in a concrete way. Is this simply an enlightening hobby or do you have significant everyday applications? What kind of cool job has you employ Bayesian Data Analysis day to day and for what benefit? How do the suits relate to such knowledge and it's beneficial application that may be well beyond their ken?

My applications have focused on noisy, high dimensional small datasets in which it is either very expensive or impossible to get more data.

One example is rare class prediction on long form text data eg phone calls, podcasts, transcripts. Other networks including neural networks and LLMs are either not flexible enough or require far too much data to achieve the necessary performance. Structured hierarchical modeling is the balance between those two extremes.

Another example is in genomic analysis. Similarly high dimensional, noisy, low data. Additionally, you don’t actually care about the predictions, you want to understand what genes or sets of genes are driving phenotypic behaviors.

I’d be happy to go into more depth via email or chat if this is something you are interested in (on my profile).

Some useful reads

[1] https://sturdystatistics.com/articles/text-classification

[2] https://pmc.ncbi.nlm.nih.gov/articles/PMC5028368/

Re: Bayesian Data Analysis, Third edition (2013) [pdf]

#65

Post by thefringthing »

oogway8020 wrote: post #61
pyyxbkshed wrote: post #20

Earlier quoted context omitted.

What is a book / course on statistics that I can go through before this so that I can understand this?

Here is one path to learn Bayesian starting from basics, assuming modern R path with tidyverse (recommended): First learn some basic probability theory: Peter K. Dunn (2024). The theory of distributions. https://bookdown.org/pkaldunn/DistTheory Then frequentist statistics: Chester Ismay, Albert Y. Kim, and Arturo Valdivia - https://moderndive.com/v2/ Mine Çetinkaya-Rundel and Johanna Hardin - https://openintrostat.gi…

I would include Richard McElreath's _Statistical Rethinking_ here after, or in combination with, _Bayes Rules!_. A translation of the code parts into the tidyverse is available free online, as are lecture videos based on the book.

Re: Bayesian Data Analysis, Third edition (2013) [pdf]

#66

Post by glial »

pyyxbkshed wrote: post #20
kianN wrote: post #13

This is my favorite book on statistics. Full stop. The author Andrew Gelman created a whole new branch of Bayesian statistics with both his theoretical work on hierarchical modeling while also publishing Stan to enable practical applications of hierarchical models. It took me about a year to work through this book on the side (including the exercises) and it provided the foundation for years of fruitful research into…

What is a book / course on statistics that I can go through before this so that I can understand this?

Doing Bayesian Data Analysis by John Kruschke (get the 2nd edition). The name is even an homage to the original.

Re: Bayesian Data Analysis, Third edition (2013) [pdf]

#68

Post by tech_ken »

j7ake wrote: post #34
canjobear wrote: post #32

Earlier quoted context omitted.

Why would it not be? You can use big data and neural nets to fit Bayesian models (variational inference).

I meant specifically the book, which doe not have any of those things you mentioned. Also nobody fits neural networks and use variation inference using any priors that aren’t some standard form that makes algorithm easy

Yeah definitely! People still need to do statistical inference in 2025 (see ex. the field of econometrics).

Re: Bayesian Data Analysis, Third edition (2013) [pdf]

#69

Post by sn9 »

pyyxbkshed wrote: post #20
kianN wrote: post #13

This is my favorite book on statistics. Full stop. The author Andrew Gelman created a whole new branch of Bayesian statistics with both his theoretical work on hierarchical modeling while also publishing Stan to enable practical applications of hierarchical models. It took me about a year to work through this book on the side (including the exercises) and it provided the foundation for years of fruitful research into…

What is a book / course on statistics that I can go through before this so that I can understand this?

For effectively and efficiently learning the calculus, linear algebra, and probability underpinning these fields, Math Academy is going to be your best resource.

Re: Bayesian Data Analysis, Third edition (2013) [pdf]

#70

Post by g9yuayon »

some_guy_nobel wrote: post #52
g9yuayon wrote: post #30

I can attest how useful Bayesian analysis is. My team recently needed to sample from many millions of items to test their qualities. The question is that given a certain budget and expectation, what's the minimum or maximum number of items that we need to sample. There was an elegant solution to this problem. What was surprising, though, was how reluctant the engineers are to learn such basic techniques. It's not lik…

What were they reluctant to learn? Why do they need to learn it? Plenty of engineers have to take an introductory stats course, but it's not clear why you'd want your engineers to learn bayesian statistics? I would be surprised if they could correctly interpret a p-value or regression coefficient, let alone one with interaction effects. (It'd be wholly useless if they could, fwiw). It'd be nice if the statisticians/'…

I guess I have a different philosophy: whoever owns the problem should learn everything necessary to solve the problem. In my case, the engineers showed no interests in learning the algorithm and the math behind it. For instance, when they built the dashboard for the testing, they omitted a few important columns and got the column names wrong. When I tested them on their understanding of the method, there was none. To say the least, my team should know enough to challenge me in case I made any mistake, or so I assume.

On a side note, I believe it is an individual's responsibility to find the coolness in their project. What's the fun of building a dashboard that I have done a thousand times? What's the fun of carrying out a routine that does not challenge me? But solving a problem in a most rigorous and generalized way? That is something in which an engineer can find some fun. Or maybe it's just me.

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