惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

G
Google Developers Blog
Google DeepMind News
Google DeepMind News
Microsoft Security Blog
Microsoft Security Blog
Martin Fowler
Martin Fowler
MyScale Blog
MyScale Blog
The GitHub Blog
The GitHub Blog
I
InfoQ
A
About on SuperTechFans
GbyAI
GbyAI
宝玉的分享
宝玉的分享
爱范儿
爱范儿
博客园 - 【当耐特】
博客园 - 司徒正美
博客园 - 聂微东
P
Proofpoint News Feed
WordPress大学
WordPress大学
云风的 BLOG
云风的 BLOG
Last Week in AI
Last Week in AI
阮一峰的网络日志
阮一峰的网络日志
B
Blog RSS Feed
Jina AI
Jina AI
aimingoo的专栏
aimingoo的专栏
J
Java Code Geeks
博客园 - 叶小钗

math.PR updates on arXiv.org

Visibility in the Boolean Model on Harmonic Manifolds Global estimates on the Brenier map Geodesics and Wandering Exponents in Brochette First-Passage Percolation State-dependent inverse-subordinator time changes of regenerative processes: Excursion structure and multiscale occupation-time limits Randomly twisted transfer operators and singular values statistics Generalized Bessel-Dunkl diffusions An almost sure invariance principle for the Takagi-van der Waerden class functions Central limit theorems for high dimensional lattice polytopes: cosmological polytopes Convergence rate estimates for semigroups and heat kernels associated with resistance forms Second-order Poincaré inequalities and localization on the Poisson space Maximum Probability of Independence in Transitive Matroids On global solutions to the semidiscrete stochastic heat equation The Poisson Tail Conjecture for primes in short intervals A Complete Spectral Analysis of the CEV Operator with Applications to Arbitrage Holographic functions and neural networks From Betting to Empirical Bernstein LIL Concentration of General Stochastic Approximation Under Heavy-Tailed Markovian Noise Pointwise Generalization in Deep Neural Networks Bayesian Latent Space Models for Graphs Are Misspecified: Toward Robust Inference via Generalized Posteriors Wasserstein bounds for denoising diffusion probabilistic models via the Föllmer process A note on connections between the Föllmer process and the denoising diffusion probabilistic model Simple Approximation and Derivative Free Inference-Time Scaling for Diffusion Models via Sequential Monte Carlo on Path Measures Diffusion-Based Stochastic Operator Networks for Uncertainty Quantification in Stochastic Partial Differential Equations A Fourier perspective on the learning dynamics of neural networks: from sample complexities to mechanistic insights Propagation of Chaos in Contextual Flow Maps Dimension-Uniform Discretization Analysis of Preconditioned Annealed Langevin Dynamics for Multimodal Gaussian Mixtures $α$-TCAV: A Unified Framework for Testing with Concept Activation Vectors Scaling Laws from Sequential Feature Recovery: A Solvable Hierarchical Model On the Limits of Latent Reuse in Diffusion Models State-of-art minibatches via novel DPP kernels: discretization, wavelets, and rough objectives
The dual Burnside process
[Submitted on 29 Oct 2025 (v1), last revised 30 Aug 2026 (this v · 2025-10-29 · via math.PR updates on arXiv.org

View PDF HTML (experimental)

Abstract:The Burnside process is a classical Markov chain for sampling uniformly from group orbits. We give a systematic study of the dual Burnside process, obtained by interchanging the roles of group elements and states. This dual chain has stationary law $\pi(g)\propto |X_g|$, is reversible, and admits a matrix factorization $Q=AB$, $K=BA$ with the classical Burnside kernel $K$. As a consequence, the two chains share all nonzero eigenvalues and have mixing times that differ by at most one step. We further establish universal Doeblin floors, orbit- and conjugacy-class lumpings, exact stabilizer/fixed-set quotient pairs, and transfer principles between $Q$ and $K$. We analyze the explicit examples of the value-permutation model $S_k$ acting on $[k]^n$ and the coordinate-permutation model $S_n$ acting on $[k]^n$. In the value-permutation model, for fixed $k\ge3$, the dual fixed-symbol-set quotient has $2^k-k-1$ states, independent of $n$, preserves the full nonzero spectrum, and has limiting nontrivial spectral radius $1/2$. These results show that the dual chain provides both a conceptual mirror to the classical Burnside process and a genuinely useful compression mechanism for symmetry-aware Markov chain Monte Carlo.

Submission history

From: Ivan Feng [view email]
[v1] Wed, 29 Oct 2025 06:14:29 UTC (165 KB)
[v2] Mon, 17 Nov 2025 17:50:20 UTC (269 KB)
[v3] Wed, 20 May 2026 17:49:28 UTC (245 KB)
[v4] Sun, 30 Aug 2026 21:30:54 UTC (39 KB)