On skew-symmetric distributions and their use in Monte Carlo sampling algorithms: coordinate-free, Gibbs-style and manifold versions of the Barker proposal

arXiv:2610.01448 · stat.CO, stat.ME, stat.ML · Submitted 2026-10-01 · Read on arXiv

stat.CO, stat.ME, stat.ML

Submitted: 2026-10-01

Updated: 2026-10-01

Terminology

• Robustness — 7× in this paper · explained in 5 episodes
• Bayesian Inference — 5× in this paper · explained in 5 episodes
• Benchmark — 2× in this paper · explained in 2 episodes
• Diffusion Process — 2× in this paper · explained in 2 episodes
• Langevin Dynamics — 2× in this paper · explained in 2 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Spectral Gap — 1× in this paper · explained in 5 episodes

Sources

• Accelerating Metropolis-Hastings algorithms by Delayed Acceptance
• On randomized step sizes in Metropolis-Hastings algorithms
• Pathwise skew-symmetric discretisation for SDEs with superlinear drift
• Foundations of locally-balanced Markov processes
• Accelerated Sampling on Discrete Spaces with Non-Reversible Markov Processes

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