Hirofumi Shiba

司馬博文
Ph.D. student in Statistics
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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 randomness can improve statistical performance.

My surname Shiba (司馬) has different kanji from the Shiba (柴) dog, although they are 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).
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