{"publisher":"Schloss Dagstuhl - Leibniz-Zentrum für Informatik","intvolume":"       261","language":[{"iso":"eng"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publication_identifier":{"issn":["1868-8969"],"isbn":["9783959772785"]},"scopus_import":"1","abstract":[{"lang":"eng","text":"A central problem in computational statistics is to convert a procedure for sampling combinatorial objects into a procedure for counting those objects, and vice versa. We will consider sampling problems which come from Gibbs distributions, which are families of probability distributions over a discrete space Ω with probability mass function of the form μ^Ω_β(ω) ∝ e^{β H(ω)} for β in an interval [β_min, β_max] and H(ω) ∈ {0} ∪ [1, n].\r\nThe partition function is the normalization factor Z(β) = ∑_{ω ∈ Ω} e^{β H(ω)}, and the log partition ratio is defined as q = (log Z(β_max))/Z(β_min)\r\nWe develop a number of algorithms to estimate the counts c_x using roughly Õ(q/ε²) samples for general Gibbs distributions and Õ(n²/ε²) samples for integer-valued distributions (ignoring some second-order terms and parameters), We show this is optimal up to logarithmic factors. We illustrate with improved algorithms for counting connected subgraphs and perfect matchings in a graph."}],"conference":{"name":"ICALP: Automata, Languages and Programming","start_date":"2023-07-10","end_date":"2023-07-14","location":"Paderborn, Germany"},"alternative_title":["LIPIcs"],"month":"07","quality_controlled":"1","license":"https://creativecommons.org/licenses/by/4.0/","date_updated":"2025-04-14T13:42:17Z","related_material":{"record":[{"status":"public","relation":"later_version","id":"18855"}]},"has_accepted_license":"1","article_processing_charge":"Yes","file":[{"success":1,"file_id":"14088","relation":"main_file","creator":"dernst","date_created":"2023-08-21T06:45:16Z","checksum":"6dee0684245bb1c524b9c955db1e933d","access_level":"open_access","file_size":917791,"date_updated":"2023-08-21T06:45:16Z","file_name":"2023_LIPIcsICALP_Harris.pdf","content_type":"application/pdf"}],"publication":"50th International Colloquium on Automata, Languages, and Programming","publication_status":"published","arxiv":1,"status":"public","oa":1,"article_number":"72","title":"Parameter estimation for Gibbs distributions","doi":"10.4230/LIPIcs.ICALP.2023.72","department":[{"_id":"VlKo"}],"date_created":"2023-08-20T22:01:14Z","volume":261,"corr_author":"1","_id":"14084","date_published":"2023-07-01T00:00:00Z","external_id":{"arxiv":["2007.10824"]},"author":[{"full_name":"Harris, David G.","last_name":"Harris","first_name":"David G."},{"last_name":"Kolmogorov","full_name":"Kolmogorov, Vladimir","id":"3D50B0BA-F248-11E8-B48F-1D18A9856A87","first_name":"Vladimir"}],"ddc":["000","510"],"tmp":{"image":"/images/cc_by.png","short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"day":"01","type":"conference","year":"2023","file_date_updated":"2023-08-21T06:45:16Z","oa_version":"Published Version","acknowledgement":"We thank Heng Guo for helpful explanations of algorithms for sampling connected subgraphs and matchings, Maksym Serbyn for bringing to our attention the Wang-Landau algorithm and its use in physics."}