Posts

Linear mixed effect models

An introduction to linear mixed effects models (LMMs) and their estimation. Aims to give an intuitive reason why we need LMMs followed by some theory and code that codes them (largely) from scratch.

Linear regression from a Gaussian process point of view

Gaussian processes have an aura of abstract complexity - "distributions over function space". I find that linking them to linear models helps reduce the abstractness.

Frontier of simulation-based inference

Some notes after reading Cranmer, Brehmer & Louppe's overview of simulation based inference.

Automatic differentiation I

Some notes and code after reading Baydin, Pearlmutter, Radul & Siskind's excellent paper on the magic that is automatic differentiation.

Causal mediation: an overview

A short introduction to causal mediation analysis and the need to think carefully about potential confounding when undertaking such analyses.

ARCH models and Bitcoin volatility

Autoregressive conditional heteroscedasticity (ARCH) models have such a long name they must be great right!? I develop some ARCH models that attempt (badly) to predict Bitcoin / $US price movements.

Structural nested mean models

This is a long post trying to understand structural nested mean models and their role in causal inference.