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The Hamming Ball Sampler

arXiv 1504.08133 ↗First posted 30 Apr 2015stat.ME stat.COcited by 16

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Abstract (arXiv)

We introduce the Hamming Ball Sampler, a novel Markov Chain Monte Carlo algorithm, for efficient inference in statistical models involving high-dimensional discrete state spaces. The sampling scheme uses an auxiliary variable construction that adaptively truncates the model space allowing iterative exploration of the full model space in polynomial time. The approach generalizes conventional Gibbs sampling schemes for discrete spaces and can be considered as a Big Data-enabled MCMC algorithm that provides an intuitive means for user-controlled balance between statistical efficiency and computational tractability. We illustrate the generic utility of our sampling algorithm through application to a range of statistical models.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 Apr 2015.

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Citing papers per year: 2021: 3; 2022: 6; 2023: 2; 2024: 3; 2025: 2.

16 papers.

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  1. 2022Generative Flow Networks for Discrete Probabilistic Modelingcited by 113
  2. 2021Oops I Took A Gradient: Scalable Sampling for Discrete Distributionscited by 92
  3. 2024Informed Correctors for Discrete Diffusion Modelscited by 57
  4. 2022A Langevin-like Sampler for Discrete Distributionscited by 42
  5. 2022Discrete Langevin Sampler via Wasserstein Gradient Flowcited by 23
  6. 2023Jointly Learning Consistent Causal Abstractions Over Multiple Interventional Distributionscited by 21
  7. 2022Enhanced gradient-based MCMC in discrete spacescited by 10
  8. 2022Optimal Scaling for Locally Balanced Proposals in Discrete Spacescited by 9
  9. 2021Sampling in Combinatorial Spaces with SurVAE Flow Augmented MCMCcited by 7
  10. 2021LSB: Local Self-Balancing MCMC in Discrete Spacescited by 7
  11. 2025Gradient GA: Gradient Genetic Algorithm for Drug Molecular Designcited by 4
  12. 2024Dimension-free Relaxation Times of Informed MCMC Samplers on Discrete Spacescited by 3
4 more
  1. 2022Bayesian Variable Selection in a Million Dimensionscited by 2
  2. 2023Importance is Important: Generalized Markov Chain Importance Sampling Methodscited by 1
  3. 2025Entropy-Guided Sampling of Flat Modes in Discrete Spacescited by 1
  4. 2024Optimal Particle-based Approximation of Discrete Distributions (OPAD)cited by 0