2 citations · 2 across the 5 of their papers we have counts for
5 papers
Folded Transport MCMC: Eliminating Label Switching by Sampling on a Fundamental Domain
Jun Hu
In Bayesian mixture models and other exchangeable-component models, the posterior is invariant under permutation of component labels, creating m! equivalent modes-the label-switchi…
Self-Certifying Transport MCMC via Dual Spectral-Gap Certificates
Jun Hu
We propose CerT-MCMC, a framework that equips learned-transport Markov chain Monte Carlo with automatic, rigorous convergence certificates. A normalising flow maps a Gaussian refer…
Non-Vacuous Certification of Transport MCMC via Oscillation-Controlled Normalizing Flows
Jun Hu
Transport MCMC trains a normalizing flow to precondition Metropolis--Hastings proposals, achieving high empirical efficiency on challenging posteriors; yet no prior work produces a…
A Broad and General Sequential Sampling Scheme
Jun Hu, Yan Zhuang
In this paper, we propose a broad and general sequential sampling scheme, which incorporates four different types of sampling procedures: i) the classic Anscombe-Chow-Robbins purel…
Bayes' Theorem under Conditional Independence
Jun Hu, Xianggui Qu
In this article we provide a substantial discussion on the statistical concept of conditional independence, which is not routinely mentioned in most elementary statistics and mathe…