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Scan Order in Gibbs Sampling: Models in Which it Matters and Bounds on
  How Much

Scan Order in Gibbs Sampling: Models in Which it Matters and Bounds on How Much

10 June 2016
Bryan D. He
Christopher De Sa
Alexia Jolicoeur-Martineau
Christopher Ré
ArXiv (abs)PDFHTML

Papers citing "Scan Order in Gibbs Sampling: Models in Which it Matters and Bounds on How Much"

18 / 18 papers shown
A fast non-reversible sampler for Bayesian finite mixture models
A fast non-reversible sampler for Bayesian finite mixture models
Filippo Ascolani
T. Rigon
124
1
0
03 Oct 2025
Cascading and Proxy Membership Inference Attacks
Cascading and Proxy Membership Inference Attacks
Yuntao Du
Jiacheng Li
Yuetian Chen
Kaiyuan Zhang
Zhizhen Yuan
Hanshen Xiao
Bruno Ribeiro
Ninghui Li
350
4
0
29 Jul 2025
Comparison Theorems for the Mixing Times of Systematic and Random Scan
  Dynamics
Comparison Theorems for the Mixing Times of Systematic and Random Scan Dynamics
Jason Gaitonde
Elchanan Mossel
169
3
0
14 Oct 2024
Is Gibbs sampling faster than Hamiltonian Monte Carlo on GLMs?
Is Gibbs sampling faster than Hamiltonian Monte Carlo on GLMs?International Conference on Artificial Intelligence and Statistics (AISTATS), 2024
Son Luu
Zuheng Xu
Nikola Surjanovic
Miguel Biron-Lattes
Trevor Campbell
Alexandre Bouchard-Côté
220
4
0
04 Oct 2024
Accelerated Markov Chain Monte Carlo Using Adaptive Weighting Scheme
Accelerated Markov Chain Monte Carlo Using Adaptive Weighting Scheme
Y Samuel Wang
Wenyu Chen
Shimin Shan
261
0
0
23 Aug 2024
Glauber Generative Model: Discrete Diffusion Models via Binary Classification
Glauber Generative Model: Discrete Diffusion Models via Binary Classification
Harshit Varma
Dheeraj M. Nagaraj
Karthikeyan Shanmugam
VLM
570
10
0
27 May 2024
Principled Gradient-based Markov Chain Monte Carlo for Text Generation
Principled Gradient-based Markov Chain Monte Carlo for Text Generation
Li Du
Afra Amini
Lucas Torroba Hennigen
Xinyan Velocity Yu
Jason Eisner
Holden Lee
Robert Bamler
BDL
264
1
0
29 Dec 2023
Solidarity of Gibbs Samplers: the spectral gap
Solidarity of Gibbs Samplers: the spectral gap
Iwona Chlebicka
K. Latuszyñski
B. Miasojedow
389
2
0
04 Apr 2023
Linear Complexity Gibbs Sampling for Generalized Labeled Multi-Bernoulli
  Filtering
Linear Complexity Gibbs Sampling for Generalized Labeled Multi-Bernoulli FilteringIEEE Transactions on Signal Processing (IEEE Trans. Signal Process.), 2022
Changbeom Shim
B. Vo
B. Vo
Jonah Ong
Diluka Moratuwage
289
24
0
29 Nov 2022
Quantum-enhanced Markov chain Monte Carlo
Quantum-enhanced Markov chain Monte CarloNature (Nature), 2022
David Layden
G. Mazzola
R. Mishmash
M. Motta
P. Wocjan
Jin-Sung Kim
S. Sheldon
151
88
0
23 Mar 2022
Fast Doubly-Adaptive MCMC to Estimate the Gibbs Partition Function with
  Weak Mixing Time Bounds
Fast Doubly-Adaptive MCMC to Estimate the Gibbs Partition Function with Weak Mixing Time BoundsNeural Information Processing Systems (NeurIPS), 2021
Shahrzad Haddadan
Zhuang Yue
Cyrus Cousins
E. Upfal
163
8
0
14 Nov 2021
Invariance Principle Meets Information Bottleneck for
  Out-of-Distribution Generalization
Invariance Principle Meets Information Bottleneck for Out-of-Distribution GeneralizationNeural Information Processing Systems (NeurIPS), 2021
Kartik Ahuja
Ethan Caballero
Dinghuai Zhang
Jean-Christophe Gagnon-Audet
Yoshua Bengio
Ioannis Mitliagkas
Irina Rish
OOD
319
331
0
11 Jun 2021
On the convergence of the Metropolis algorithm with fixed-order updates
  for multivariate binary probability distributions
On the convergence of the Metropolis algorithm with fixed-order updates for multivariate binary probability distributions
Kai Brügge
Asja Fischer
Christian Igel
92
0
0
26 Jun 2020
A Hybrid Scan Gibbs Sampler for Bayesian Models with Latent Variables
A Hybrid Scan Gibbs Sampler for Bayesian Models with Latent Variables
Grant Backlund
J. Hobert
Yeun Ji Jung
Kshitij Khare
243
2
0
27 Aug 2018
Minibatch Gibbs Sampling on Large Graphical Models
Minibatch Gibbs Sampling on Large Graphical Models
Christopher De Sa
Vincent Chen
W. Wong
233
21
0
15 Jun 2018
Improving Gibbs Sampler Scan Quality with DoGS
Improving Gibbs Sampler Scan Quality with DoGS
Alexia Jolicoeur-Martineau
Lester W. Mackey
222
7
0
18 Jul 2017
Layerwise Systematic Scan: Deep Boltzmann Machines and Beyond
Layerwise Systematic Scan: Deep Boltzmann Machines and Beyond
Heng Guo
Kaan Kara
Ce Zhang
165
7
0
15 May 2017
Optimal compromise between incompatible conditional probability
  distributions, with application to Objective Bayesian Kriging
Optimal compromise between incompatible conditional probability distributions, with application to Objective Bayesian Kriging
Joseph Muré
299
11
0
21 Mar 2017
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