Homepage
Latest Posts
WALNUTS = Within-orbit Adaptive Leapfrog No-U-Turn Sampler
In many Bayesian inference problems, the geometry of the posterior distribution can vary dramatically in scale. A classic example is Neal’s funnel, where the state-of-the-art algorithm, the No-U-Turn Sampler (NUTS), … Read More
A Locally Adaptive, Gradient-Free MCMC Method Inspired by the No-U-Turn Sampler
Markov Chain Monte Carlo (MCMC) methods are fundamental for sampling from complex probability distributions, but many widely used algorithms either rely on gradients (like NUTS) and/or struggle with high-dimensional, multi-scale … Read More
Why Go Coordinate-Free in Monte Carlo and Optimization?
Traditional methods like Gibbs sampling or randomized Kaczmarz rely heavily on specific coordinate systems, which can limit their efficiency—especially in ill-conditioned settings. But what happens when we step away from … Read More