Interpreting the confidence interval in front of you – josue rodriguez
I go over the most common interpretation of a confidence interval, and discuss an alternative, potentially more useful one.
Confidence intervals have a valid interpretation within the context of a single sample, and without a need to reference an infinite series of hypothetically repeated experiments.
For whatever reason this perspective is under-discussed, so I explain and discuss here!
16.12.2024 16:52 — 👍 1 🔁 0 💬 0 📌 0
Two of the most valuable skills I developed through my psychology curriculum are perspective-taking and empathy. It’s remarkable how often technical work is communicated poorly simply because the audience’s perspective was either overlooked or flat out disregarded.
05.12.2024 23:09 — 👍 2 🔁 0 💬 0 📌 0
Connecting Scalar and Matrix Notation in OLS Regression – josue rodriguez
I go over OLS regression in matrix notation, and show how it connects to the scalar notation of OLS regression.
Have you ever wondered why the "normal" OLS regression formulas and the matrix notation give the same estimates? I realized I’d never seen it explained, so I dug in and wrote about it. If you're curious too, you can check out my post: josue-rodriguez.github.io/posts/2024-1...
21.11.2024 19:20 — 👍 3 🔁 1 💬 0 📌 0
Official channel of {easystats}, a collection of #rstats 📦s with a unifying and consistent framework for statistical modeling, visualization, and reporting.
“Statistics are like sausages. It’s better not to see them being made, unless you use easystats.”
Psychopathology | Personality | Quant Methods
Professor of Psychology and Psychiatry | Eisenberg Family Depression Center | University of Michigan
Editor | Journal of Psychopathology and Clinical Science
Founder and Instructor | www.smart-workshops.com
Technology (product/ux), art (ceramics) and psychology at Teachers College, Columbia University
https://www.michaelheadrick.com/
Outdoorsy UXer. Workshop design coach & consultant. Lives with anxiety. Runs on espresso and Star Wars. She/her. 🇨🇦🏳️🌈 Founder of CanUX. Best known for popularizing the phrase "UX Theatre". https://spydergrrl.com
Author of the award-winning Ancillary Justice. Lives in St Louis.
🎵Senior Lecturer in Music Psychology & Head of Enterprise, Royal Northern College of Music UK
🧬 Research: audiences, live music, music & Parkinson’s, music & neuroscience
🎓PhD Uni of Cambridge
📊Accountant 🎷Saxophonist
Www.WhyHumansNeedMusic.com
Doing Bayesian stuff in #rustlang and #julialang. Seattle
Theory & practice of probabilistic programming. Current: MIT Probabilistic Computing Project; Fall '25: Incoming Asst. Prof. at Yale CS
Associate Professor (UHD) at the University of Amsterdam. Probabilistic methods, deep learning, and their applications in science in engineering.
Exploring {Probabilistic Programming w/ Typed λ-Calculi; Lean; Program Synthesis; Self-Improving A.I.; Evolutionary Genetics} @ umontreal. Ph.D. USherbrooke
Assistant Prof in ML @ KTH 🇸🇪.
Previous: Aalto University 🇫🇮, TU Graz 🇦🇹, originally from 🇩🇪.
Doing: Reliable ML | uncertainty stuff | Bayesian stats | probabilistic circuits
https://trappmartin.github.io/
Probabilistic machine Learning, causal inference, language models. Teach at http://Altdeep.ai & @Northeastern, work at @MSFTResearch.
Research Fellow at Aalto University. Open source contributor #ArviZ, #Bambi, #Kulprit, #PreliZ, #PyMC, #PyMC-BART.
Support me at https://ko-fi.com/aloctavodia
https://bayes.club/@aloctavodia
The Bayesian Consultancy • Using PyMC to solve your most challenging data science problems • http://pymc-labs.com
Statistician, Associate Professor (Lektor) at University of Gothenburg and Chalmers; inference and conditional distributions for anything
https://mschauer.github.io
http://orcid.org/0000-0003-3310-7915
[ˈmoː/r/ɪts ˈʃaʊ̯ɐ]
Research Scientist at MIT studying interactions of randomness and computation
Runner, biker, hiker. Software engineer @DeepMind, and open source enthusiast. Sometimes crafts things out of wood. he/his.
🎓 CS Prof at UCLA
🧠 Researching reasoning and learning in artificial intelligence: tactable deep generative models, probabilistic circuits, probabilistic programming, neurosymbolic AI
https://web.cs.ucla.edu/~guyvdb/
Official account for the ArviZ project. We provide #FOSS tools for exploratory analysis of #Bayesian models in #Python and #JuliaLang
www.arviz.org