2 citations · 5 across the 8 of their papers we have counts for
5 papers · 1 filter
Rapid Mixing of Stereographic MCMC for Heavy-Tailed Sampling
Federica Milinanni, Tyler Farghly, Jun Yang
Sampling from high-dimensional, heavy-tailed distributions is a fundamental challenge in computational statistics, as many standard Markov chain Monte Carlo (MCMC) methods mix poor…
Markov Chain Monte Carlo with Diffusion Paths
Han Chen, Sifan Liu, Jun Yang
Sampling from multimodal distributions is a longstanding challenge for classical local Markov chain Monte Carlo (MCMC) methods. A popular remedy is to introduce a sequence of inter…
Sub-Cauchy Sampling: Escaping the Dark Side of the Moon
Sebastiano Grazzi, Sifan Liu, Gareth O. Roberts +1
We introduce a Markov chain Monte Carlo algorithm based on Sub-Cauchy Projection, a geometric transformation that generalizes stereographic projection by mapping Euclidean space in…
Stereographic Multiple-Try Metropolis
Zhihao Wang, Jun Yang
Multiple-proposal MCMC algorithms have recently gained attention for their potential to improve performance, especially through parallel implementation on modern hardware. We intro…
Gaussian Approximation and Output Analysis for High-Dimensional MCMC
Ardjen Pengel, Jun Yang, Zhou Zhou
The widespread use of Markov Chain Monte Carlo (MCMC) methods for high-dimensional applications has motivated research into the scalability of these algorithms with respect to the…