🎙️ Ep. 133 is out now!
Alex Andorra chats with Sean P
& Adrian Seyboldt about making Bayesian models more efficient without losing rigor — zero-sum constraints, Cholesky tricks, practical wins & more
#Bayesianstats
Learning about PyMC makes me want to become a statistician.. super interesting way to think about data, but so much goes into building a good model! So many rabbit holes.
Bayesian modelling is clearly super powerful though and seems to offer some answers to some of the most intractable problems with black-box ML. A reliable model with known and understandable inputs is invaluable for certain use cases.
Bae in the fast lane: Master Bayes Regression in just 20 minutes! 🚗📊
Join Patrick Ward and me in #TidyX Episode 171 for a speed run through rstanarm and Tidybayes, predicting car mileage based on weight 🛣️
Bit.ly/TidyX_Ep171
Those days when nothing works, no one knows why, and the computer is struggling.
where to begin?🤔
You may ask yourself that when initializing a #MCMC sampler.
A first idea may be to use the mean of the data as starting point - that's a bad idea though! Let @aseyboldt explain why in this piece from episode 74 🧑🏫
#BayesianStats
we're kicking the New Year off with a very #Bayesian episode 🎆
#NUTS 🌰sampling, a new #ZeroSumNormal #distribution, #PyMC, the Bayesian workflow and more is all covered in episode 74 with @aseyboldt @pymc_devs #BayesianStats #modelling
Time for a small #Introduction! I'm a PhD student at #AarhusUniversity and #UniversityOfYork.
My PhD looks at how infants discover and explore the sounds of their first language using a combination of methods from #acoustics, #phonetics, #Bayesianstats, and #metascience. Relatedly, I'm a big fan of all things #Rstats, #Bayesian, #bigteamscience, and #openscience!
When I'm not programming or writing, I'm usually with my violin, trying to make it sound like a trumpet (i.e., #jazz #blues ).
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