Blog
Essays, technical walkthroughs, and modeling notes.
March 29, 2026
Sampling Markov Random Fields in NumPyro
Markov random fields (MRFs) are an interesting class of statistical models that originate in physics. They can be thought of as a collection of random variables where we allow arbitrary joint dependencies between variables. These dependencies...
March 15, 2026
When Are Claims Evidence?
I recently watched a back and forth between two YouTubers who generally engage in discussions on philosophy, epistemology, and debate. The topic was about whether or not claims are evidence. One of the YouTubers (A) adamantly...
December 22, 2025
Sequential Neural Likelihood Estimation with C++
A recent pre-print by Blassel et al. (2025) renewed my interest in simulation-based inference. In this post, I revisit my sequential neural likelihood estimator for the partially observed stochastic Lotka–Volterra model, reimplemented in C++ using LibTorch...
May 22, 2025
Modeling Stochastic Lotka-Volterra using Sequential Neural Likelihood Estimation
Many interesting systems in science can’t be described by closed-form probability distributions. This makes them hard to analyze using classical statistical methods. One common example in biology is the stochastic Lotka–Volterra model, which describes the dynamics...
April 9, 2025
Variational Bayes from a Generalized Bayesian Inference Perspective
Markov Chain Monte Carlo (MCMC) is an amazing tool for Bayesian inference, but it’s unfortunately quite computationally intensive. Often, that’s fine, and we’re willing to wait for an asymptotically exact answer. But that isn’t always the...