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Rapidly Mixing Multiple-try Metropolis Algorithms for Model Selection Problems
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Rapidly Mixing Multiple-try Metropolis Algorithms for Model Selection Problems

Hyunwoong Chang
Changwoo J. Lee
Z. Luo
H. Sang
Quan Zhou
ArXiv (abs)PDFHTML

Papers citing "Rapidly Mixing Multiple-try Metropolis Algorithms for Model Selection Problems"

7 / 7 papers shown
Title
Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs
Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs
Jie Hu
Yi-Ting Ma
Do Young Eun
64
0
0
23 May 2025
Stereographic Multi-Try Metropolis Algorithms for Heavy-tailed Sampling
Stereographic Multi-Try Metropolis Algorithms for Heavy-tailed Sampling
Zhihao Wang
Jun Yang
53
0
0
18 May 2025
On the fundamental limitations of multiproposal Markov chain Monte Carlo algorithms
On the fundamental limitations of multiproposal Markov chain Monte Carlo algorithms
Francesco Pozza
Giacomo Zanella
96
2
0
30 Oct 2024
Dimension-free Relaxation Times of Informed MCMC Samplers on Discrete Spaces
Dimension-free Relaxation Times of Informed MCMC Samplers on Discrete Spaces
Hyunwoong Chang
Quan Zhou
79
6
0
05 Apr 2024
Importance is Important: A Guide to Informed Importance Tempering Methods
Importance is Important: A Guide to Informed Importance Tempering Methods
Guanxun Li
Aaron Smith
Quan Zhou
77
2
0
13 Apr 2023
Improving multiple-try Metropolis with local balancing
Improving multiple-try Metropolis with local balancing
Philippe Gagnon
Florian Maire
Giacomo Zanella
85
11
0
21 Nov 2022
Order-based Structure Learning without Score Equivalence
Order-based Structure Learning without Score Equivalence
Hyunwoong Chang
James Cai
Quan Zhou
CMLOffRL
80
3
0
10 Feb 2022