Bryan

Still learning.

[Richard McElreath 🐈‍⬛ @rlmcelreath]

The website for the book is online, and we've placed the 20% discount code there: https://t.co/f472XjM6cf

[Rob Calver @RobCalver5]

Very excited to announce that the #BayesianWorkflow book by @StatModeling, @avehtari, @rlmcelreath et al publishes in June! https://t.co/srs8cpBmxK #RStats #DataScience #Bayesian @mcmc_stan

[kache @yacineMTB]

you can outsource your thinking
but you cannot outsource your understanding

[藤井亮輔 🦒 Ryosuke Fujii (Ryo) @Ryo_epidemiol]

論文閲覧できるリンクはこちらです（こちらはシェア拡散頂いて構いません）🔗

https://t.co/gUGrjWEPoU

[Andrew Gelman et al. @StatModeling]

I don’t like the Wikipedia definition of Bayesian statistics. The Merriam-Webster definition is much better!
https://t.co/U35L1yznez

[Sam Levey @SamHLevey]

I posted a new video! It's the first in a series of videos that will explain the concept of "Degrees of Freedom" from statistics, in a way that actually makes sense.

I'm really excited about this one - check it out! https://t.co/e4o4Pj0zbJ

[Maarten van Smeden @MaartenvSmeden]

Challenge: explain degrees of freedom in one Tweet

[Vincent Arel-Bundock @VincentAB]

Whoa—my book is up for pre-order!

Model to meaning: How to interpret Statistical & ML Models in #RStats and #PyData

The book presents an ultra-simple and powerful workflow to make sense of ± any model you fit

Note: The web version stays free forever.

https://t.co/fpDmSdDWov

[Richard McElreath 🐈‍⬛ @rlmcelreath]

I realize the world is burning, but here is the abstract of a talk I will give multiple times in different formats this month. With the feedback, I hope to write up something substantial on the theme in the remainder of the year.

[Richard McElreath 🐈‍⬛ @rlmcelreath]

Do you find the way statistics relates to scientific questions confusing and arbitrary? Do you like drawing owls? Then my free lectures are here for you

[Price is going to Vibechella @uPrice11]

https://t.co/mN5bpEYLc3

[Frank Harrell @f2harrell]

#Statistics thought of the day: https://t.co/RQ3PgIolVI

[Andrew Gelman et al. @StatModeling]

Bayesian inference is not what you think it is!
https://t.co/cyUtT8GkZ1

[Luis M. A. Bettencourt @BettencourtLuis]

All course materials (slides, readings, code, data)
for Introduction to Urban Science (MIT Press, also in Chinese) are now freely available on Github: https://t.co/tUzaDveRR5

Make them your own and keep in touch!
Happy 2025!

[Raphael Nishimura @rnishimura]

OMG, I want to live in this article! Spiegehalter puts in words exactly how I approach probability: it doesn't exist as an objective measure, but it's still useful to pretend it does.
In a way, it feels like Calibrated Bayes, my favorite kind of Bayesian!

[essen @essenaccount]

there's probably a pdf out there that would change your whole life

[Shravan Vasishth @ShravanVasishth]

Our Introduction to Bayesian Data Analysis for Cognitive Science (with Nicenboim and Schad) is now in production with CRC Press. It will remain freely available here:

https://t.co/8ngMjfqH7D

[Sean J. Taylor @seanjtaylor]

I haven't had a chance to read yet, but looks like @StefanWager wrote the causal inference book I've always wanted! https://t.co/8rMD4Z9vVF

[Eugene Plavskin @mossomest]

@ChelseaParlett 100% agree with this but also want to plug https://t.co/PNBIBzpbCX, which ended up filling a LOT of that gap for me