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Related Experiment Video

Updated: Feb 14, 2026

Characterization and Application of Passive Samplers for Monitoring of Pesticides in Water

Characterization and Application of Passive Samplers for Monitoring of Pesticides in Water

Published on: August 3, 2016

The Hamming Ball Sampler.

Michalis K Titsias¹, Christopher Yau^2,3

¹Department of Informatics, Athens University of Economics and Business, Athens, Greece.

Journal of the American Statistical Association

|February 20, 2018

Summary

This summary is machine-generated.

We developed the Hamming ball sampler, a new Markov chain Monte Carlo method for efficient inference in complex statistical models. This algorithm improves exploration of high-dimensional discrete spaces, balancing statistical efficiency and computational cost.

Keywords:

Bayesian Discrete state spaces Markov chain Monte Carlo

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Area of Science:

Statistics
Computational Science

Background:

Statistical models with high-dimensional discrete state spaces present significant inference challenges.
Conventional sampling methods like Gibbs sampling can be computationally intensive or inefficient in these settings.

Purpose of the Study:

To introduce a novel Markov chain Monte Carlo (MCMC) algorithm, the Hamming ball sampler.
To enable efficient inference in statistical models with high-dimensional discrete state spaces.
To provide a user-controlled balance between statistical efficiency and computational tractability.

Main Methods:

The Hamming ball sampler utilizes an auxiliary variable construction.
It adaptively truncates the model space for iterative exploration.
The method generalizes conventional Gibbs sampling for discrete spaces.

Main Results:

The proposed sampler facilitates efficient exploration of the full model space.
It offers an intuitive approach for balancing statistical efficiency and computational demands.
Demonstrated utility across a range of statistical models.

Conclusions:

The Hamming ball sampler is a versatile and efficient tool for Bayesian inference in high-dimensional discrete models.
This novel MCMC algorithm enhances computational tractability without sacrificing statistical accuracy.
The method offers a flexible alternative to existing sampling techniques.