Hirofumi Shiba

司馬博文
Ph.D. student in Statistics
Google Scholar arXiv Github

Institute of Statistical Mathematics (ISM), Tokyo, Japan

Feel free to call me Hiro. I'm advised by Kengo Kamatani.

I work mainly in Bayesian computation, studying how stochastic dynamics enable modern large-scale inference.

Interests

  • Computational Statistics & Machine Learning
  • Monte Carlo Methods
  • Piecewise Deterministic Markov Processes
  • Stochastic Gradient Descent

I am interested in the dynamical aspects of these algorithms, especially how weak or structured noise can improve statistical performance.

Shiba dog (柴) and I (司馬) are different in kanji but equally charming.

Software

I maintain the Julia package PDMPFlux.jl, and also contribute to the R package YUIMA.

Selected Publications

Diffusive Scaling Limits of Forward Event-Chain Monte Carlo: Provably Efficient Exploration with Partial Refreshment

Hirofumi Shiba, Kengo Kamatani. Submitted to the Annals of Applied Probability. 2026

Many PDMP methods have been proposed. We develop a high-dimensional scaling framework to compare their asymptotic efficiency (effective sample size per event).
No matching items