Markov Chain Monte Carlo

Also known as MCMC

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Latest papers 145

27 papers in the last four weeks, up 50% on the four weeks before. 0.2% of all new papers.

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Guiding Posterior Exploration with Optimizer-Derived Geometry
Jul 28, 2026Moritz Schlager, Emanuel Sommer, Thomas Möllenhoff +1Bayesian Neural Networks Markov Chain Monte Carlo
TUM, MCML · LMU Munich, MCML · RIKEN AIP
Practical advantage beyond the quadratic speedup limit with fully-quantum walks
Jul 24, 2026Massimiliano Incudini, Guglielmo MazzolaMarkov Chain Monte Carlo Ising Model
International School for Advanced Studies (SISSA), via Bonomea 265, 34136 Trieste, Italy
Parallel Noising in Neural Markov Logic Networks
Jul 21, 2026Peter Jung, Giuseppe Marra, Ondrej KuzelkaMarkov Chain Monte Carlo Graph Generation
Czech Technical University, Prague, Czech Republic · Department of Computer Science, KU Leuven, Leuven, Belgium
BayesPO: Bayesian Prompt Optimization via Parallel-Tempered Gradient-Guided Discrete MCMC
Jul 17, 2026Junjie Zhou, Zhijian OuMarkov Chain Monte Carlo LLM Prompting
Speech Processing and Machine Intelligence (SPMI) Lab Tsinghua University, China
Neural Non-Equilibrium Hamiltonian Monte Carlo for Corrected Boltzmann Sampling
Jul 17, 2026Moxian QianMarkov Chain Monte Carlo
Helmholtz Institute Mainz Johannes Gutenberg University Mainz Institute of Molecular Biology (IMB) Mainz, Germany
Delocalization of bias in unadjusted Hamiltonian Monte Carlo and underdamped Langevin
Jul 16, 2026Yifan Chen, Xiaoou Cheng, Jonathan Niles-Weed +1Markov Chain Monte Carlo Langevin Dynamics
Department of Mathematics, University of California, Los Angeles, CA 90095, USA · Courant Institute, New York University, NY 10012, USA
Beyond the $d^{2.5}$ -mixing bound for Dikin walks on polytopes
Jul 15, 2026Yunbum KookMarkov Chain Monte Carlo
Georgia Tech
Accelerated Mixing Time of Randomized Hamiltonian Monte Carlo
Jul 14, 2026Siddharth Mitra, Vishwak Srinivasan, Xiuyuan Wang +1Markov Chain Monte Carlo Log-Concave Sampling
Department of Computer Science, Yale University. · Department of Electrical Engineering and Computer Science, MIT.
An End-to-End Hybrid Quantum--Classical Sampling Workflow for Discrete Markov Random Fields: A Reproducible Case Study
Jul 10, 2026Arul Rhik MazumderMarkov Chain Monte Carlo Markov Random Fields
School of Computer Science
Gradient-free Riemannian Langevin Sampler
Jul 8, 2026Ricardo Baptista, Olivier ZahmMarkov Chain Monte Carlo
Department of Statistical Sciences, University of Toronto, Canada · UGA, Inria, CNRS, Grenoble INP*, LJK, 38000 Grenoble, France
Non-Asymptotic Error Bounds for SMC with Biased Proposals: Application to Conditional Diffusion Sampling
Jul 6, 2026Stanislas Strasman, Gabriel Victorino Cardoso, Sylvain Le Corff +2Markov Chain Monte Carlo Markov Models
SU, LPSM · Sorbonne Université and Université Paris Cité, CNRS, LPSM, F-75005 Paris, France · LPSM +2
Optimal scaling of MCMC algorithms: the Hamiltonian approach
Jul 1, 2026P. Dobson, J. M. Sanz-Serna, K. C. ZygalakisMarkov Chain Monte Carlo
Maxwell Institute for Mathematical Sciences and Mathematics Department Heriot-Watt University, Edinburgh, EH14 4AS, UK · Departamento de Matem´aticas, Universidad Carlos III de Madrid Avenida Universidad 30, 28911 Legan´es, Madrid · Maxwell Institute for Mathematical Sciences and School of Mathematics University of Edinburgh, Peter Guthrie Tait Rd, EH9 3FD, Edinburgh
Diffusion-warm sampling of the XY model enables fast thermalization at scale
Jun 29, 2026Sehmimul Hoque, Roger Melko, Pooya RonaghMarkov Chain Monte Carlo Diffusion Sampling
Institute for Quantum Computing, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada · Department of Physics and Astronomy, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada · Perimeter Institute for Theoretical Physics, Waterloo, Ontario N2L 2Y5, Canada +1
Variance Reduction for Stochastic Gradient Generalized Non-reversible Langevin Monte Carlo Algorithms
Jun 27, 2026Bingye Ni, Xiaoyu Wang, Yingli Wang +1Markov Chain Monte Carlo Stochastic Gradient Langevin Dynamics
FinTech Thrust, Hong Kong University of Science and Technology (Guangzhou), Guangzhou, Guangdong, People's Republic of China · School of Mathematical Science, Fudan University, Shanghai, People's Republic of China · Department of Mathematics, Florida State University, Tallahassee, Florida, United States of America
Simulation-based inference for rapid Bayesian parameter estimation in epidemiological models: a comparison with MCMC
Jun 25, 2026Alina Bazarova, Johann Fredrik Jadebeck, Henrik Zunker +5Markov Chain Monte Carlo Simulation-Based Inference
Forschungszentrum Jülich, Jülich Supercomputing Centre, Jülich, Germany · Helmholtz AI, Germany · Forschungszentrum Jülich, Institute of Bio- and Geosciences, Jülich, Germany +3
Exact Schur-Sylvester Dimensionality Reductions for Non-Smooth Stochastic Complexity and Manifold Sampling
Jun 22, 2026Trenton Lau, Gary P. T. ChoiMarkov Chain Monte Carlo
Department of Mathematics, The Chinese University of Hong Kong
Synergizing Physically Constrained MCMC and Chemical-Informed Gaussian Processes for Reaction Network Discovery
Jun 22, 2026Runzhe Liu, Zihao Wang, Wenbo Yang +1Markov Chain Monte Carlo Bayesian Optimization
State Key Laboratory of Fine Chemicals, Frontier Science Center for Smart Materials, Dalian University of Technology, Dalian, 116024, China · Dalian Key Laboratory of Intelligent Chemistry, CR Belt and Road Joint Laboratory on Intelligent Chemistry and Advanced Materials of Liaoning Province, School of Chemistry, Dalian University of Technology, Dalian, 116024, China
Variational Consensus Monte Carlo for Bayesian Mixture
Jun 17, 2026Julie Fendler, Francesca L. Crowe, Tom Marshall +2Markov Chain Monte Carlo Federated Learning Aggregation
MRC Biostatistics Unit, University of Cambridge · Institute of Applied Health Research, University of Birmingham
Structured Inference with Large Language Gibbs
Jun 17, 2026Sanghyeok Choi, Henry Gouk, Esmeralda S. WhitammerMarkov Chain Monte Carlo LLM Inference
University of Edinburgh, School of Informatics · 2CIFAR Fellow, Learning in Machines and Brains
Stop the Sampler! Classifier-Based Adaptive Stopping for Sampling Kernels
Jun 15, 2026Kirill Korolev, Nikita Morozov, Stepan Pavlenko +2Markov Chain Monte Carlo Bayesian Inference
1HSE University · University of Edinburgh · 3CIFAR Fellow.
Data assimilation for subsurface flow using latent diffusion model parameterization: performance of ensemble-Kalman and Monte Carlo techniques
Jun 9, 2026Guido Di Federico, Wenchao Teng, Louis J. DurlofskyMarkov Chain Monte Carlo Latent Diffusion Models
Department of Energy Science & Engineering, Stanford University, Stanford, CA, 94305, USA
Sample Where You Struggle: Sharpening Base Model Reasoning via Entropy-Guided Power Sampling
Jun 7, 2026Hong Guo, Nianhui Guo, Christoph Meinel +1Markov Chain Monte Carlo Inference-Time Scaling
Hasso Plattner Institut · German University of Digital Science
Folded Transport MCMC: Eliminating Label Switching by Sampling on a Fundamental Domain
Jun 3, 2026Jun HuMarkov Chain Monte Carlo Bayesian Mixture Models
Wuhan University of Technology
Regularized Large Neighborhood Search
Jun 1, 2026Germain Vivier-Ardisson, Laurent Demonet, Axel Parmentier +1Markov Chain Monte Carlo Metaheuristic Optimization
Google DeepMind & CERMICS · Google Research · CERMICS, ENPC, CNRS, IPP +1
Non-Vacuous Certification of Transport MCMC via Oscillation-Controlled Normalizing Flows
May 31, 2026Jun HuMarkov Chain Monte Carlo Normalizing Flows
China Sanya Science and Education Innovation Park, Wuhan University of Technology, Sanya 572025, China · School of Civil Engineering and Architecture, Wuhan University of Technology, Wuhan 430070, China
Large-scale Uncertainty Quantification for Latent Variable Models Using Subsampling Markov Chain Monte Carlo
May 29, 2026Xiaoyu Wang, Jonathan H. HugginsMarkov Chain Monte Carlo Stochastic Gradient Langevin Dynamics
Department of Mathematics & Statistics, Boston University · Faculty of Computing & Data Sciences, Boston University
Accurate Large-sample Uncertainty Quantification using Stochastic Gradient Markov Chain Monte Carlo
May 29, 2026Yu Wang, Jie Ding, Jonathan H. HugginsMarkov Chain Monte Carlo Stochastic Gradient Langevin Dynamics
Department of Mathematics & Statistics, Boston University · Questrom School of Business, Boston University · Faculty of Computing & Data Sciences, Boston University
Improved Guarantees for Langevin Monte Carlo with Average Smoothness
May 29, 2026Arnak S. Dalalyan, Avetik KaragulyanMarkov Chain Monte Carlo Stochastic Gradient Langevin Dynamics
CREST, ENSAE Paris, Institut Polytechnique de Paris and MBZUAI · L2S, CNRS, Centrale Supélec, Université Paris-Saclay
Parallel Tempering Initial Sampling in Inference-Time Reward Alignment
May 29, 2026Myeongjun Oh, Gwangho Kim, Sungyoon LeeDiffusion Model Alignment Markov Chain Monte Carlo
Department of Artificial Intelligence 2Department of Computer Science Hanyang University, Seoul, Korea
Self-Certifying Transport MCMC via Dual Spectral-Gap Certificates
May 29, 2026Jun HuMarkov Chain Monte Carlo Convergence Analysis
Wuhan University of Technology, Wuhan, China

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Guiding Posterior Exploration with Optimizer-Derived Geometry

Moritz Schlager, Emanuel Sommer, Thomas Möllenhoff, David Rügamer

TUM, MCML · LMU Munich, MCML · RIKEN AIP

Bayesian Neural Networks Markov Chain Monte Carlo Uncertainty Quantification Posterior Sampling

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Sampling-based methods offer a principled approach to uncertainty quantification in Bayesian neural networks. Their practical use, however, is often challenged by the computational cost of exploring high-dimensional and multimodal posterior distributions. To overcome these difficulties, Bayesian Deep Ensembles, i.e., warmstarting the sampling from several optimized solutions, have proven to be an effective strategy. In this paper, we demonstrate that curvature estimates computed during the warmstart as a byproduct in adaptive optimizers such as AdamW can inform the sampling phase at negligible additional cost. Specifically, our proposed preconditioned sampling strategy based on optimizer-derived geometries can substantially reduce or even eliminate the need for a lengthy sampling burn-in phase and leads to greater numerical stability. This approach consistently maintains or improves predictive performance and uncertainty quantification without any additional computational costs. We confirm the consistency of our findings across various datasets and network architectures.