Adam Fleischhacker @preposterior@qoto.org
People and data compelling action. Author of "Intro to Bayesian Business Analytics in the R Eco-system" (see https:://causact.com) and "Persuasive Python: Unifying Narrative, Math, and Code" (forthcoming).
Made an introductory 📕(draft) about using Python for Bayesian Inference and unifying narrative, math, and code. People seem to find it helpful. Check it out. Feedback encouraged.
#DataScience #Python #bayes #Stats #probabilisticprogramming
Public #OpenScience online lecture for #OSCSummerSchool23
🔥 Prof Richard McElreath @rlmcelreath
🎯 Science as amateur software development
📅 13.09 at 09:00 CEST
More info: https://malikaihle.github.io/OSC-Open-Research-Summer-School-2023/
Free registration: https://pretix.osc.lmu.de/lmu-osc/lectures/
Know anyone who wants to teach #rstats and #Bayesian statistics out of a business school? Please make them aware of the second edition of my textbook. Students love it and it is a great entry point into data science. https://www.causact.com/
For better or worse, I self-illustrate my textbooks despite having zero artistic ability. On my latest edition (coming out next week), I used NightCafe AI image generation to get all artsy with my book cover. I hope you like the AI art... here is a pre- and post-ai book cover comparison.
Updates to supporting textbook coming soon. But dag_numpyro() is now a drop-in replacement for dag_greta(). Non-updated book here: www.causact.com
Bayesian Additive Regression Trees (BART) seem like a robust way to do causal inference; easily-queried black boxes with built-in uncertainty quantification of causal effects. Anyone using BART in practice?
Check out Sal's outlook on AI in education. A segment where a student engages the iconic Jay Gatsby is great. I'm presently immersed in Atlas Shrugged and appreciate being able to request "a book review of Atlas Shrugged written by AOC."
https://youtu.be/hJP5GqnTrNo
Interested in graphs for #causalinference? Start the year right by taking our *free* online course "Draw Your Assumptions Before Your Conclusions".
Join the 80,000 people who have already taken the #CausalDiagramsCourse.
Registration is open. No math background required. All content is now freely available.
The course was made possible by April Opoliner and the team at HarvardX. Thanks to edX for making online learning accessible for everybody.
https://www.edx.org/course/causal-diagrams-draw-your-assumptions-before-your
DAGs, Golems, and Owls: Statistical Rethinking 2023 Lecture 1 (of 20). No hard work in this introductory lecture, just a conceptual outline and some dank memes. Lecture 2 later this week introduces Bayesian inference. https://www.youtube.com/watch?v=FdnMWdICdRs&list=PLDcUM9US4XdPz-KxHM4XHt7uUVGWWVSus&index=1
Most enjoyable article-writing experience ever thx to gr8 co-authors🔥.
We find how to value integer-based data prior to seeing it; a magic trick with quadratic loss being hidden secret to get closed-form🤫. Link to article in advance of publication:
https://pubsonline.informs.org/doi/10.1287/deca.2022.0462#.Y7Lvk3xoofQ.twitter
For all my new financial services analytics students, this episode is a fantastic window into data science in your world. It is so good. @Cmastication is a fascinating human being with gift for storytelling. Make sure you LISTEN!
@ericmjl has great content all over YouTube. However, it is this blog post that has proved most helpful to me while wrestling with shape dimensions in NumPyro : "Reasoning about Shapes and Probability Distributions". So good! https://ericmjl.github.io/blog/2019/5/29/reasoning-about-shapes-and-probability-distributions/
I have this notion that the power of data science comes from unifying narrative, math, and code. (narrative is a proxy for real-world issues and phenomena). Expertise in just two of the three is very limiting. Been working on a #Python book to explore this more thoroughly. If you want to take a peek, here is the link:
https://www.persuasivepython.com/
Let me know what you think. Three new chapters will be released on Thursday.
People and data compelling action. Author of "Intro to Bayesian Business Analytics in the R Eco-system" (see https:://causact.com) and "Persuasive Python: Unifying Narrative, Math, and Code" (forthcoming).
