Lectures: All recorded lectures hosted on YouTube Playlist **note we are departing from the original 10 week format (see schedule).
Discussion: Online weekly zoom meetings (info below) and 24/7 rolling live or asynchronous discussion on the #statisticalrethinking channel hosted on the University of Bayes Discord server.
Best to do both, if you can
- Zoom Meeting ID: 946 3248 2480
- Password: 124631
- Link: https://ucsf.zoom.us/j/94632482480?pwd=STZDaC92ZG5wS3NaSCtzTlBMZlRJQT09
- Ask/answer/discuss questions or clarify concepts
- Great options for people whose schedules or time zones unfortunately don't match up with our Wed at 9AM.
- Discord SR channel invite link here: https://discord.gg/UqddWFT8
- Watch this video tutorial on how to get access to the channel using the link above
Please see this extended schedule below. Weekly problem sets are are discussed in Zoom meetings and Discord channel. We want you to come to meetings even if you couldn't finish the problem set for the week.
Lecture playlist on Youtube: Statistical Rethinking 2022
| Meet # | Date | Chapter | Lectures |
|---|---|---|---|
| 1 | 9/28/2022 | 1 | [1] <The Golem of Prague> <(Slides)> [2] <Bayesian Inference> <(Slides)> |
| 2 | 10/5/2022 | 2 | continued as above |
| 3 | 10/12/2022 | 3 | continued as above |
| 4 | 10/19/2022 | 3 | continued as above |
| 5 | 10/26/2022 | 4 | [3] <Basic Regression> <(Slides)> [4] <Categories & Curves> <(Slides)> |
| 6 | 11/2/2022 | 4 | continued as above |
| 7 | 11/9/2022 | 5 | continued as above |
| 8 | 11/16/2022 | 5 | [5] <Elemental Confounds> <(Slides)> [6] <Good & Bad Controls> <(Slides)> |
| 9 | 11/23/2022 | 6 | continued as above |
| 10 | 11/30/2022 | 6 | continued as above |
| 11 | 12/7/2022 | Take a break! | [7] <Overfitting> <(Slides)> [8] <Markov chain Monte Carlo> <(Slides)> |
| 12 | 12/14/2022 | 7 | continued as above |
| 13 | 12/21/2022 | 7 | continued as above |
| 14 | 12/28/2022 | 8 | continued as above |
| 15 | 1/4/2023 | Take a break! | continued as above |
| 16 | 1/11/2023 | Take a break! | continued as above |
| 17 | 1/18/2023 | 8 | continued as above |
| 18 | 1/25/2023 | 9 | continued as above |
| 19 | 2/1/2023 | 9 | continued as above |
| 20 | 2/8/2023 | 10 | [9] <Logistic and Binomial GLMs> <(Slides)> [10] <Sensitivity and Poisson GLMs> <(Slides)> |
| 21 | 2/15/2023 | 10 | continued as above |
| 22 | 2/22/2023 | 11 | continued as above |
| 23 | 3/1/2023 | 11 | continued as above |
| 24 | 3/8/2023 | 12 | [11] <Ordered Categories> <(Slides)> [12] <Multilevel Models> <(Slides)> |
| 25 | 3/15/2023 | 12 | continued as above |
| 26 | 3/22/2023 | 13 | continued as above |
| 27 | 3/29/2023 | 13 | [13] <Multi-Multilevel Models> <(Slides)> [14] <Correlated varying effects> <(Slides)> |
| 28 | 4/5/2023 | 14 | continued as above |
| 29 | 4/12/2023 | 14 | [15] <Social Networks> <(Slides)> [16] <Gaussian Processes> <(Slides)> |
| 30 | 4/19/2023 | 15 | continued as above |
| 4/26/2023 | 15 | continued as above | |
| 27 | 5/3/2023 | 16 | [19] <Beyond GLMs> <(Slides)> [20] <Horoscopes> <(Slides)> |
| 28 | 5/10/2023 | 16 | continued as above |
| 29 | 5/17/2023 | 17 | continued as above |
| 30 | 5/24/2023 | 17 | continued as above |
We are using the 2nd edition of, <Statistical Rethinking>. This edition is a major update I do not reccommend the first edition (as it is missing key content).
You will have to find a way to get this book. Check your library, many have subscriptions to the official electronic version. Purchasing a copy of the second edition is a really good idea if you can at all afford it. If your resources are limited such that you wouldn't be able to participate, you might consider asking someone in the community about a temporary pdf to use until you're able to purchase. Richard McElreath has been incredibly generous in making all of these materials free of charge. It's a simple and not particularly large of sum of money* to say 'thank you' with.
*We are and inter-class and international community. Your situation may be such that purchasing a copy is a big deal; if so, my encouragement to support McElreath financially is not directed at you. We support you and want you to be here!
This course teaches data analysis, but it focuses on scientific models first. The unfortunate truth about data is that nothing much can be done with it, until we say what caused it. We will prioritize conceptual, causal models and precise questions about those models. We will use Bayesian data analysis to connect scientific models to evidence. And we will learn powerful computational tools for coping with high-dimension, imperfect data of the kind that biologists and social scientists face.
This course involves a lot of scripting. Students can engage with the material using either the original R code examples or one of several conversions to other computing environments. The conversions are not always exact, but they are rather complete. Each option is listed below. I also list conversions <here>.
For those who want to use the original R code examples in the print book, you need to install the rethinking R package. The code is all on github https://github.com/rmcelreath/rethinking/ and there are additional details about the package there, including information about using the more-up-to-date cmdstanr instead of rstan as the underlying MCMC engine.
The <Tidyverse/brms> conversion is very high quality and complete through Chapter 14.
The <Python/PyMC3> conversion is quite complete. There are also at least two NumPyro conversions: <NumPyro1> <NumPyro2>. And there is this <TensorFlow Probability>.
The <Julia/Turing> conversion is not as complete, but is growing fast and presents the Rethinking examples in multiple Julia engines, including the great <TuringLang>.
The are several other conversions. See the full list at https://xcelab.net/rm/statistical-rethinking/.
I will also post problem sets and solutions. Check the folders at the top of the repository.