Posterior sampling by source-space MCMC via prior-based few-step transport maps

arXiv:2610.01034 · stat.ML, cs.AI, cs.LG · Submitted 2026-10-01 · Read on arXiv

stat.ML, cs.AI, cs.LG

Submitted: 2026-10-01

Updated: 2026-10-01

Code: https://github.com/Lyy-iiis/imeanflow

Terminology

• Bayesian Inference — 8× in this paper · explained in 5 episodes
• Adaptation — 7× in this paper · explained in 1 episode
• Flow Matching — 7× in this paper · explained in 7 episodes
• Generative Model — 5× in this paper · explained in 2 episodes
• Generative Models — 5× in this paper · explained in 2 episodes
• Hamiltonian Monte Carlo — 5× in this paper
• Inverse Problem — 5× in this paper · explained in 2 episodes
• Consistency — 4× in this paper · explained in 3 episodes
• Generative Modeling — 3× in this paper · explained in 3 episodes
• Source Parallel Tempering — 3× in this paper
• Adam — 2× in this paper · explained in 2 episodes
• Anisotropy — 2× in this paper · explained in 3 episodes
• Cosine Similarity — 2× in this paper · explained in 3 episodes
• Distillation — 2× in this paper · explained in 2 episodes
• Generative AI — 2× in this paper · explained in 2 episodes
• Hilbert Space — 2× in this paper · explained in 2 episodes
• Optimal Transport — 2× in this paper · explained in 5 episodes
• Uncertainty Quantification — 2× in this paper · explained in 8 episodes
• Wasserstein Distance — 2× in this paper · explained in 4 episodes
• Alignment — 1× in this paper · explained in 1 episode
• Approximation Bound — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Normalizing Flow — 1× in this paper · explained in 3 episodes
• Normalizing Flows — 1× in this paper · explained in 4 episodes
• Systematic Bias — 1× in this paper · explained in 3 episodes
• Transformer — 1× in this paper · explained in 2 episodes

Sources

• D-Flow: Differentiating through Flows for Controlled Generation
• Optimizing The Integrator Step Size for Hamiltonian Monte Carlo
• Flow map matching with stochastic interpolants: A mathematical framework for consistency models
• Preconditioned One-Step Generative Modeling for Bayesian Inverse Problems in Function Spaces
• NeuTra-lizing Bad Geometry in Hamiltonian Monte Carlo Using Neural Transport
• If at First You Don't Succeed, Try, Try Again: Faithful Diffusion-based Text-to-Image Generation by Selection
• On Variance Reduction in Learning Mean Flows
• Bayesian Inference with Generative Adversarial Network Priors
• BayesFlow: Amortized Bayesian Workflows With Neural Networks
• Denoising Score-Matching for Uncertainty Quantification in Inverse Problems

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