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See this MLSS '20 presentation and all recommended bibliography [1], in particular top left box in slide 123. For rigorous modeling, Gelman et al. books are gr
by nextos 3y ago
See this MLSS '20 presentation and all recommended bibliography [1], in particular top left box in slide 123.
For rigorous modeling, Gelman et al. books are great [2-3].
[1] https://drive.google.com/file/d/1lPePNMGMEKoaDvxiftc8hcy-rFpobXeJ/view https://drive.google.com/file/d/1lPePNMGMEKoaDvxiftc8hcy-rFp...
[2] https://avehtari.github.io/ROS-Examples https://avehtari.github.io/ROS-Examples
[3] http://www.stat.columbia.edu/~gelman/book http://www.stat.columbia.edu/~gelman/book
- quanto 3y agoAh, thank you. I did hear about Gelman's books before, but I haven't read them in detail. Will check those out. Do you have references/books for non-Bayesian approach as well?
- nextos 3y agoYou could try this one: https://hastie.su.domains/CASI_files/PDF/casi.pdf https://hastie.su.domains/CASI_files/PDF/casi.pdf However, IMHO many things come more naturally in a Bayesian setting, such as hierarchical models.