I’m especially proud of this article I wrote about Gaussian Processes for the Recast blog! 🥳

GPs are super interesting, but it’s not easy to wrap your head around them at first 🤔

This is a medium level (more intuition than math) introduction to GPs for time series.

getrecast.com/gaussian-pro...

How many years after undergrad stats did you discover that common statistical tests are linear models? lindeloev.github.io/tests-as-lin...

New blog post! Let's say you've measured two variables repeatedly and want to investigate how one affects the other over time. Here are some recommendations for how to do that well.

www.the100.ci/2025/06/25/r...

PSA that Sequential Monte Carlo ("particle filtering") can be used on general models now. You can motivate it as implementing gradient descent in Fisher-Rao metric, like HMC is abstractly Wasserstein gradient descent. This leads to tuning parameter advice, too: more MCMC steps.
New Chopin seminar:

Self learning is hard and doing it in a way which works for me required lots of trial and error. I've tried

1. Reading books, watching lectures if available and doing problem sets (McElreath's Statistical rethinking, Gil Strang's linear algebra)

2. Reading as a reference, sometimes annotating

I completed my Master's in Applied Econometrics 🎉 To celebrate, I'm re-posting a summary of my research on using Simulation Based Calibration (SBC) to compare MCMC algorithms in the context of stochastic volatility (SV) models arxiv.org/abs/2402.12384 🧵:

I can summarise how I learn things with a single comic, especially as a new parent:

Source: www.facebook.com/webcomicname...

I'm starting an informal statistics book club on discord. First off is Multilevel/Hierarchical models by Gelman and Hill (2007). Channel is there to ask questions, discuss topics or problem sets in the book. Read at your own pace, start wherever you want. Open invite for those who want to join :)