{"paper":{"title":"Bayesian nonparametric analysis of reversible Markov chains","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.TH"],"primary_cat":"math.ST","authors_text":"Lorenzo Trippa, Sergio Bacallado, Stefano Favaro","submitted_at":"2013-06-06T07:08:51Z","abstract_excerpt":"We introduce a three-parameter random walk with reinforcement, called the $(\\theta,\\alpha,\\beta)$ scheme, which generalizes the linearly edge reinforced random walk to uncountable spaces. The parameter $\\beta$ smoothly tunes the $(\\theta,\\alpha,\\beta)$ scheme between this edge reinforced random walk and the classical exchangeable two-parameter Hoppe urn scheme, while the parameters $\\alpha$ and $\\theta$ modulate how many states are typically visited. Resorting to de Finetti's theorem for Markov chains, we use the $(\\theta,\\alpha,\\beta)$ scheme to define a nonparametric prior for Bayesian analy"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1306.1318","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}