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Probabilistic-Programming-and-Bayesian-Methods-for-Hackers
aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)
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I recommend Bayesian Methods for hackers [1]. It doesn’t go too deep on theory but I feel like it’s well written and has really good coded up examples of theory being applied to problems. It has a relatively narrow scope, but I find myself reaching for methods I learned from the author frequently.
[1] https://github.com/CamDavidsonPilon/Probabilistic-Programmin...
I recommend Bayesian Methods for hackers [1]. It doesn’t go too deep on theory but I feel like it’s well written and has really good coded up examples of theory being applied to problems. It has a relatively narrow scope, but I find myself reaching for methods I learned from the author frequently.
[1] https://github.com/CamDavidsonPilon/Probabilistic-Programmin...
Here's an excerpt of a comment I previously made on Hacker News:
I'm a Ph.D. student in operations research (OR). My suggestion would be to first build a strong foundation in linear programming. This will introduce you to the notion of duality, which is heavily emphasized in many mathematical programming courses. Here's a good open-source book on linear programming written by Jon Lee, the current editor of Mathematical Programming A: https://github.com/jon77lee/JLee_LinearOptimizationBook
Then I'd suggest studying more general methods for continuous and convex optimization. The book I see mentioned a lot is Convex Optimization by Boyd and Vandenberghe, although we didn't use this in our coursework. Instead, we used a lot of the material presented here: http://mitmgmtfaculty.mit.edu/rfreund/educationalactivities/
If you read the above (or any other two books on linear programming and convex optimization), you'll probably have a better idea of what you want to study next and how you want to go about it. The next natural step would be to study combinatorial (i.e., integer or mixed-integer) optimization. (Jon Lee has another book on this subject; I've also heard good things about the Schrijver book.)