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I think you will enjoy Efron & Hastie's Computer Age Statistical Inference: Algorithms, Evidence and Data Science. https://web.stanford.edu/~hastie/CASI/ https
by ArtWomb 8y ago
I think you will enjoy Efron & Hastie's Computer Age Statistical Inference: Algorithms, Evidence and Data Science.
https://web.stanford.edu/~hastie/CASI/ https://web.stanford.edu/~hastie/CASI/
MIT 6.008 Introduction to Inference also goes over many of the classic problems such as Cookie Jar, Monte Hall, Fair Dice, One-Armed Bandits, etc. with tons of labs.
I find this stuff easier to grok by just writing code and running a simulation. To that end, I'd setup an Anacondas environment and start experimenting with PyMC3. As your intuition suggests, there's no downside to mastery of probabilistic programming. Best of luck!
https://docs.pymc.io/ https://docs.pymc.io/
- kgwgk 8y agoI think Efron & Hastie may be a bit too advanced and terse for the OP. They cover many things in not so many pages and "our intention was to maintain a technical level of discussion appropriate to Masters’-level statisticians or first-year PhD students." But given that they distribute the PDF for free it's worth checking out. Hastie, Tibshirani & Friedman's The Elements of Statistical Learning and the watered-down and more practical Introduction to Statistical Learning are also nice. All of them can be downloaded from https://web.stanford.edu/~hastie/pub.htm https://web.stanford.edu/~hastie/pub.htm
- achompas 8y agoElements of Statistical Learning is the other text I came in here to recommend. One of my most valuable activities in grad school was printing and studying each chapter of EoSL. It's a comprehensive text on the fundamentals of statistics and machine learning, a solid foundation for the cutting-edge techniques relying on deep learning and reinforcement learning.