Andy Timm

This post is a brief introduction to nutpieR’s new “Bring Your Own Density Kernel” feature.

My new R package regrake is now live!

If you wish to make an apple pie LLM from scratch, you must first invent the universe systems considerations. — Carl Sagan, probably In December, I finished working through Stanford NLP’s CS336: Language Models From Scratch.

Ahead of starting my current gig last February, I was reviewing a bit about multilevel models, and came across this fascinating footnote in Richard McElreath’s Statistical Rethinking: See Jaynes (1986) for an entertaining example concerning the beer prefe…

Jim Savage calls the MaxDiff model of discrete choice a “mathematically incorrect model of a psychologically incoherent concept”1.

This is a draft of a paper I wrote in grad school on whether formation of voting habits exhibits any heterogeneity by race, party, or gender, using data from the Florida voter file.

I recently gave a talk on Regularized Raking at the New York Open Statistical Programming Meetup.

Note: Since writing this post, Pathfinder (described below as well in Section 5) has become available in CmdStan, which poses a problem for this concluding post to the series.

This is section 6 in my series on using Variational Inference to speed up relatively complex Bayesian models like Multilevel Regression and Poststratification without the approximation being of disastrously poor quality.

This is section 5 in my series on using Variational Inference to speed up relatively complex Bayesian models like Multilevel Regression and Poststratification without the approximation being of disastrously poor quality.

This is section 4 in my series on using Variational Inference to speed up relatively complex Bayesian models like Multilevel Regression and Poststratification without the approximation being of disastrously poor quality.

Note: I’ve gotten a lot more pessimistic about how generally useful the alternatives to simple KL-Divergence are on their own since writing this post.

This is the second post in my series on using Variational Inference to speed up relatively complex Bayesian models like Multilevel Regression and Poststratification without the approximation being of disastrously poor quality.

This post introduces a series I intend to write, exploring using Variational Inference to massively speed up running complex survey estimation models like variants of Multilevel Regression and Poststratification while aiming to keep approximation error fr…

Race is a defining part of political identity in the United States, and so it should be no surprise that accurately modeling race can be beneficial for many political campaign activities.