I understand that "just write a book" might come off as glib, especially for profs who teach 3+ classes per semester. So I don't mean that everyone has to, or should, but
1. Writing 150 pages that are targeted for your class and your students is easier than writing "the bible" for your field.
2. The quality of the first draft might not be great, but if you are getting feedback and constantly improving, it's not long before you are better off than using one of the expensive tomes.
3. If you start with other free material, you can get off to a fast start (several people have now written books that started with my material, and then evolved beyond recognition).
This is too disaligned with the criteria that are important to having a successful career as a professor. The problem isn't doing the work, it's that it takes a lot of time and is generally not a priority for the university and department vs research.
I've had some professors create fully self-contained slide decks for their course with references. Other professors teaching the same course often shared slides. Occasionally a textbook emerges from this material, but not most of the time. I think this is an approximation of your idea and probably the closest we'll get in practice.
Edit: Okay, I see in another child comment that you are a CS professor — how do you make the time to do both?
That's true. I have the good fortune to work at a college (Olin College) that recognizes that my work developing textbooks is aligned with the mission of the institution, and can have as much external impact as research. But you are right -- most places give professors zero credit for writing textbooks.
Just wanted to thank you for all the stuff you have given us. I have found your books to be both delightful and enlightening, and the fact you give them away is pretty astounding. I myself buy the printed copies just to support this work, however I know many many people not of means that I point your resources to with great success.
I don't know if you hear it a lot or not enough, but thank you, sincerely. Solid material you got here.
This is very similar to my method. The first time I teach a class, I draft some notes. After the class, I fix the problems, or at least flag them for next time. The second time through, I add, remove, edit, refine, etc., based on feedback from students and my own observation (and, often, what I have learned since last time :)
Many of my examples start with code, so the first draft of the chapter is mostly explaining the worked example.
It's not very different from the work most profs do when they are developing a class, but at the end you have a book that has co-evolved to fit your students and the learning goals.
Ignore the haters -- Think Bayes is going to be awesome!
Just kidding (mostly), but your point is correct: there is no book that is right for all audiences. But if you can program, and the mathematical approach to this material doesn't do it for you, this book might.
I need to write a preface to answer this question, but the most important prereq is Python programming. The premise of the series is that if you can program (in any language) you can use that skill as leverage to learn about other topics.
The problem with the Girl Named Florida is that the ambiguous wording is more confusing than the math.
Ambiguous: "In a family with two children, what are the chances, if one of the children is a girl named Florida, that both children are girls?"
More clear, and emphasizing the importance of precise wording when discussing probability: "Among families with two children, with at least one of the children being a girl named Florida, what portion have two girls? (Assume that all names are chosen randomly from the same distribution, independently of all other factors; and sex is determined as by a fair coin toss.)"
Yes, that's exactly what the objective of this book is! I am not using computation out of necessity, but rather because I think it provides leverage for understanding the concepts, and learning to (as you say) compose traditional models and build new ones.
As the book comes along, I am finding that many ideas that are hard to explain and understand mathematically can be very easy to express computationally, especially using discrete approximations to continuous distributions.
I'd recommend using as many real examples as possible. Things like forecasting, product recommendations, topic modeling, etc. While you can conceptually explain how Bayesian statistics is a unified recipe, it's incredibly hard to have this sink in with toy problems. This is especially true since many people using traditional tools are actually using advanced methods to solve real problems, so when they start reading about urns or doors it all comes across as rather academic. That's sad because the benefit of Bayesian coherency is mostly that it leads to a highly productive mode of practical data analysis.
Definitely shoot me an email at tristan@senseplatform.com if you're interested in the computational side of this area. At Sense (http://www.senseplatform.com), we're working on making applied Bayesian analysis as amazing as it should be.
