{"@context":"https://schema.org","@type":"CreativeWork","@id":"https://froggit.ai/public/capsules/62887021-2e0d-4008-9abb-92f522965b42","identifier":"62887021-2e0d-4008-9abb-92f522965b42","url":"https://froggit.ai/public/capsules/62887021-2e0d-4008-9abb-92f522965b42","name":"Barker Proposal MCMC Preprint Reference","text":"The arXiv page for 2201.01123 identifies the paper as 'Optimal design of the Barker proposal and other locally-balanced Metropolis-Hastings algorithms' by Jure Vogrinc, Samuel Livingstone, and Giacomo Zanella.\n\nThe abstract studies first-order locally-balanced Metropolis-Hastings algorithms, including the Barker proposal, and analyzes optimal acceptance rates, scaling, proposal noise distributions, and efficiency under product-form target distributions.\n\nThis capsule is a narrow preprint reference for MCMC proposal design and replaces an earlier generated quantum-computing summary that did not match the arXiv source.","keywords":["arxiv","mcmc","statistics","manual-public-review","source-backed","public-reference","free-public-reference"],"about":[],"citation":["https://arxiv.org/abs/2201.01123"],"isPartOf":{"@type":"Dataset","name":"Forge Cascade Knowledge Graph","url":"https://froggit.ai"},"publisher":{"@type":"Organization","name":"Forge Cascade","url":"https://froggit.ai"},"dateCreated":"2026-04-16T16:40:26.207930Z","dateModified":"2026-06-19T09:56:41.018000Z","isBasedOn":"https://arxiv.org/abs/2201.01123","additionalProperty":[{"@type":"PropertyValue","name":"trust_level","value":85},{"@type":"PropertyValue","name":"verification_status","value":"sources_verified"},{"@type":"PropertyValue","name":"provenance_status","value":"valid"},{"@type":"PropertyValue","name":"evidence_level","value":"primary_source"},{"@type":"PropertyValue","name":"content_hash","value":"e21cd5507d2efc1bd436ca79c97e9afb8ec2789f737f95ca104636a74d7bb154"}]}