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New Research Analyzes Gibbs Sampler Mixing Times in Probit Regression

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Researchers have published a paper on arXiv detailing the mixing times of data-augmentation Gibbs samplers used in Bayesian probit regression. The study provides explicit non-asymptotic bounds on these mixing times, which are dependent on the design matrix and prior precision. The findings identify scenarios where mixing times remain bounded even as the number of data points and parameters increase, offering guidance on selecting prior distributions for faster convergence. An empirical analysis using coupling techniques supports the effectiveness of these bounds in predicting practical behaviors. AI

RANK_REASON This is a research paper published on arXiv. [lever_c_demoted from research: ic=1 ai=0.4]

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New Research Analyzes Gibbs Sampler Mixing Times in Probit Regression

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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Filippo Ascolani, Giacomo Zanella ·

    Mixing times of data-augmentation Gibbs samplers for high-dimensional probit regression

    arXiv:2505.14343v2 Announce Type: replace-cross Abstract: We investigate the convergence properties of popular data-augmentation samplers for Baye\-sian probit regression. Leveraging recent results on Gibbs samplers for log-concave targets, we provide simple and explicit non-asym…

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