Markov Chain Monte Carlo
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Related Articles from SNS
True Self-Avoiding Walk for Accelerating Markov-Chain Monte Carlo Integration
Announce Type: cross Abstract: We study true self-avoiding walk (TSAW) as a mechanism for improving empirical integral estimation via Markov chain Monte Carlo (MCMC). We consider finite-state adaptive sampling dynamics associated with an irreducible Markov kernel $P$ on a finite set, with stationary distribution $\pi$, in which the transition probabilities are penalized according to empirical overuse. Our main result is that the empirical occupation counts $L_t(i)$ and transition counts...
Bayesian Inference of Nonlinear Malaria Dynamics in Ghana via an Ensemble Markov Chain Monte Carlo Sampler
arXiv:2606.00783v1 Announce Type: cross Abstract: Reliable quantification of malaria dynamics in sub-Saharan Africa is hindered by short, noisy, and spatially heterogeneous surveillance records. In Ghana, health-facility data from 2014 to 2023 reveal non-linear and age-specific fluctuations in hospital admissions, yet existing approaches struggle to capture stochastic variability or provide credible uncertainty bounds. This study develops a Bayesian nonlinear inference framework that...
Discrete-symmetry-adapted Markov chain Monte Carlo for the electro-elasticity of polymers: chain taut, collapse, and order
arXiv:2205.00028v2 Announce Type: replace-cross Abstract: Dielectric elastomers are promising for soft robotics and wearable electronics and sensors, but their use is hindered by the high electric fields required. Maximizing electromechanical coupling through molecular mechanisms is essential. However, progress on the role of dipole-dipole interactions between monomers has been limited, in part because the resulting energy landscapes, characterized by multiple symmetric wells separated by...
Finite-n Estimate of Dedekind Numbers by Layer-Ratio Monte Carlo
Announce Type: cross Abstract: Dedekind's problem counts monotone Boolean functions, equivalently downsets of a Boolean lattice. We recast this enumeration as a finite layer-ratio reconstruction problem for the Whitney numbers of the ranked ideal lattice. An exact adjacent-layer double count expresses each layer ratio through local averages of the number of addable elements and the number of removable elements.
Local and Global Contraction Principles for MCMC Mixing
arXiv:2606.03033v1 Announce Type: new Abstract: We develop a contraction-based framework for proving mixing-time bounds for Markov chain Monte Carlo algorithms. The framework is built around global and local contraction coefficients of Markov kernels under the $\mathsf E_\gamma$-divergence with $\gamma\ge1$. For projected Langevin Monte Carlo on a compact convex domain, we show that Gaussian smoothing yields an explicit global contraction coefficient for the $\mathsf E_\gamma$-divergence....
Self-Certifying Transport MCMC via Dual Spectral-Gap Certificates
arXiv:2605.30722v1 Announce Type: new Abstract: We propose CerT-MCMC, a framework that equips learned-transport Markov chain Monte Carlo with automatic, rigorous convergence certificates. A normalising flow maps a Gaussian reference to an approximation of the target posterior; the same flow then serves as both the independence Metropolis-Hastings proposal and the basis for a computable spectral-gap bound. We develop two complementary certificates.
Self-Certifying Transport MCMC via Dual Spectral-Gap Certificates
arXiv:2605.30722v2 Announce Type: replace Abstract: We propose CerT-MCMC, a framework that equips learned-transport Markov chain Monte Carlo with automatic, rigorous convergence certificates. A normalising flow maps a Gaussian reference to an approximation of the target posterior; the same flow then serves as both the independence Metropolis-Hastings proposal and the basis for a computable spectral-gap bound. We develop two complementary certificates.
Adversarial Configurations for the ReCom Transition Function
arXiv:2606.01333v1 Announce Type: new Abstract: ReCom is a leading Markov Chain Monte Carlo algorithm for sampling balanced graph partitions in computational redistricting. At each step, its transition function proposes a new partition by merging two adjacent districts and if possible re-splitting the conjoined region. The transition function is efficient in practice, however, it is unknown whether it is guaranteed to run in polynomial time.
Bayesian estimation of spectral parameters of the 6.7-GHz methanol maser G339.884-1.259 from GRAO observations
arXiv:2606.00768v1 Announce Type: cross Abstract: Accurate decomposition of methanol maser spectra is essential for understanding high-mass star-forming regions, especially in complex blended spectra where small differences alter physical interpretation. Conventional Gaussian fitting often fails to capture non-Gaussian structure and lacks uncertainty quantification. We develop a Bayesian spectral decomposition framework using Gaussian, Lorentzian, and Voigt profiles with Markov Chain Monte...
Asymptotically Optimal Sequential Testing with Markovian Data
arXiv:2602.17587v2 Announce Type: replace-cross Abstract: We study one-sided and $\alpha$-correct sequential hypothesis testing for data generated by an ergodic, finite-state Markov chain. The null hypothesis is that the unknown transition matrix belongs to a prescribed set $P$ of stochastic matrices, and the alternative corresponds to a disjoint set $Q$. We establish a non-asymptotic instance-dependent lower bound on the expected stopping time of any valid sequential test under the...