arXiv (OAI Expanded)
open access
Optimal Scaling of Langevin Proposals with Generalized Acceptance Rules
Langevin-based Markov chain Monte Carlo (MCMC) algorithms use gradient information to improve sampling, particularly in high dimensions. Classical optimal scaling theory for these algorithms has largely focused on the Metropolis-Hastings (MH) acceptance rule. However, there has been a recent surge i
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Near-Linear Accuracy Bounds for Moreau--Yosida Unadjusted Langevin Sampling#1154457
- Near-Linear Accuracy Bounds for Moreau--Yosida Unadjusted Langevin Sampling#1136823
- Iterated sampling importance resampling with adaptive number of proposals#785094
- Iterated sampling importance resampling with adaptive number of proposals#783504
- Quasi-Monte Carlo Beyond Hardy-Krause II: $(1 + \varepsilon)n$ Samples Suffice#998496
Record · ID 1123351
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive