I am interested in this topic, but this textbook is too daunting for me. What I'd love is a crash course on Bayesian methods for the working systems performance engineer. If you, dear reader, happen to be familiar with both domains: what would you include in such a course, and can you recommend any existing resources for self-study?
Bayesian Data Analysis, Third edition (2013) [pdf]
11–20 of 71 posts
Re: Bayesian Data Analysis, Third edition (2013) [pdf]
#12I'm a fan of the stats blog hosted by Columbia that Gelman is the main contributor to: https://statmodeling.stat.columbia.edu
Thanks for sharing, any particular articles that had last impact on you?
idk about impact, but here are a couple I liked:
- https://statmodeling.stat.columbia.edu/2025/08/25/what-writi...
- https://statmodeling.stat.columbia.edu/2025/09/04/assembling...
Re: Bayesian Data Analysis, Third edition (2013) [pdf]
#13This 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 hierarchical Bayesian models. It’s a definitely not an introductory read, but for any looking to advance their statistical toolkit, I cannot recommend this book highly enough.
As a starting point, I’d strongly suggest the first 5 chapters for an excellent introduction to Gelman’s modeling philosophy, and then jumping around the table of contents to any topics that look interesting.
Re: Bayesian Data Analysis, Third edition (2013) [pdf]
#14I am interested in this topic, but this textbook is too daunting for me. What I'd love is a crash course on Bayesian methods for the working systems performance engineer. If you, dear reader, happen to be familiar with both domains: what would you include in such a course, and can you recommend any existing resources for self-study?
My go to for teaching statistics is Statistical Rethinking. It’s basically a course in how to actually thing about modeling: what you’re really looking for is analyzing a hypothesis, and a model may be consistent with a number of hypotheses, figuring out what hypotheses any given model implies is the hard/fun part, and this book teaches you that. The only drawback is that it’s not free. (Although there are excellent lectures by the author available for free on YouTube. These are worth watching even if you don’t get the book.)
I also recommend Gelman’s (one of the authors of the linked book) Regression and Other Stories as a more approachable text for this content.
Think Bayes and Bayesian Methods for Hackers are introductory books from a beginner coming from a programming background.
If you want something more from the ML world that heavily emphasizes the benefits of probabilistic (Bayesian) methods, I highly recommend Kevin Murphy’s Probabilistic Machine Learning. I have only read the first edition before he split it into two volumes and expanded it, but I’ve only heard good things about the new volumes too.
Re: Bayesian Data Analysis, Third edition (2013) [pdf]
#15This 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…
Is there a good book that covers statistics as it is applied to testing - like for medical research or as optimization or manufacturing or whatever?
Re: Bayesian Data Analysis, Third edition (2013) [pdf]
#16I'm a fan of the stats blog hosted by Columbia that Gelman is the main contributor to: https://statmodeling.stat.columbia.edu
Thanks for sharing, any particular articles that had last impact on you?
Here are the articles that were popular on HN over the years:
Re: Bayesian Data Analysis, Third edition (2013) [pdf]
#17I am interested in this topic, but this textbook is too daunting for me. What I'd love is a crash course on Bayesian methods for the working systems performance engineer. If you, dear reader, happen to be familiar with both domains: what would you include in such a course, and can you recommend any existing resources for self-study?
My go to for teaching statistics is Statistical Rethinking. It’s basically a course in how to actually thing about modeling: what you’re really looking for is analyzing a hypothesis, and a model may be consistent with a number of hypotheses, figuring out what hypotheses any given model implies is the hard/fun part, and this book teaches you that. The only drawback is that it’s not free. (Although there are excellent…
Yep 100% came here to say the same. Helped me a lot during the PhD to get a better understanding of statistics.
Re: Bayesian Data Analysis, Third edition (2013) [pdf]
#18This 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…
Is there a good book that covers statistics as it is applied to testing - like for medical research or as optimization or manufacturing or whatever?
This book is very relevant to those fields. There is a common choice in statistics to either stratify or aggregate your dataset.
There is an example in his book discussing efficacy trials across seven hospitals. If you stratify the data, you lose a lot of confidence, if you aggregate the data, you end up just modeling the difference between hospitals.
Hierarchical modeling allows you to split your dataset under a single unified model. This is really powerful for extracting signal for noise because you can split your dataset according to potential confounding variables eg the hospital from which the data was collected.
I am writing this on my phone so apologies for the lack of links, but in short the approach in this book is extremely relevant of medical testing.
Re: Bayesian Data Analysis, Third edition (2013) [pdf]
#19This 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…
Is there a good book that covers statistics as it is applied to testing - like for medical research or as optimization or manufacturing or whatever?
The key insight to recognize is that within the Bayesian framework hypothesis testing is parameter estimation. Your certainty in the outcome of the test is your posterior probability over the test-relevant parameters.
Once you realize this you can easily develop very sophisticated testing models (if necessary) that are also easy to understand and reason about. This dramatically simplifies.
If you're looking for a specific book recommendation Statistical Rethinking does a good job covering this at length and Bayesian Statistics the Fun Way is a more beginner friendly book that covers the basics of Bayesian hypothesis testing.
Re: Bayesian Data Analysis, Third edition (2013) [pdf]
#20This 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?