{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,21]],"date-time":"2025-12-21T10:24:00Z","timestamp":1766312640236,"version":"build-2065373602"},"reference-count":16,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2015,10,30]],"date-time":"2015-10-30T00:00:00Z","timestamp":1446163200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>In recent years, the maximum entropy principle has been applied to a wide range of different fields, often successfully. While these works are usually focussed on cross-disciplinary applications, the point of this letter is instead to reconsider a fundamental point of kinetic theory. Namely, we shall re-examine the Stosszahlansatz leading to the irreversible Boltzmann equation at the light of the MaxEnt principle. We assert that this way of thinking allows to move one step further than the factorization hypothesis and provides a coherent\u2014though implicit\u2014closure scheme for the two-particle distribution function. Such higher-order dependences are believed to open the way to a deeper understanding of fluctuating phenomena.<\/jats:p>","DOI":"10.3390\/e17117522","type":"journal-article","created":{"date-parts":[[2015,11,2]],"date-time":"2015-11-02T02:53:57Z","timestamp":1446432837000},"page":"7522-7529","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A Truncation Scheme for the BBGKY2 Equation"],"prefix":"10.3390","volume":"17","author":[{"given":"Gregor","family":"Chliamovitch","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of Geneva, Route de Drize 7, 1227 Geneva, Switzerland"},{"name":"Department of Theoretical Physics, University of Geneva, Quai Ernest-Ansermet 24, 1211 Geneva, Switzerland"}]},{"given":"Orestis","family":"Malaspinas","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Geneva, Route de Drize 7, 1227 Geneva, Switzerland"}]},{"given":"Bastien","family":"Chopard","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Geneva, Route de Drize 7, 1227 Geneva, Switzerland"}]}],"member":"1968","published-online":{"date-parts":[[2015,10,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Jaynes, E.T. (1957). Information Theory and Statistical Mechanics. Phys. Rev., 106.","DOI":"10.1103\/PhysRev.106.620"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Jaynes, E.T. (1957). Information Theory and Statistical Mechanics. II. Phys. Rev., 108.","DOI":"10.1103\/PhysRev.108.171"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"238701","DOI":"10.1103\/PhysRevLett.91.238701","article-title":"Network Information and Connected Correlations","volume":"91","author":"Schneidman","year":"2003","journal-title":"Phys. Rev. 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Lett."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"867","DOI":"10.3390\/e11040867","article-title":"Maximum Entropy Estimation of Transition Probabilities of Reversible Markov Chains","volume":"11","year":"2009","journal-title":"Entropy"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"138101","DOI":"10.1103\/PhysRevLett.102.138101","article-title":"Prediction of Spatiotemporal Patterns of Neural Activity from Pairwise Correlations","volume":"102","author":"Marre","year":"2009","journal-title":"Phys. Rev. Lett."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"042707","DOI":"10.1103\/PhysRevE.89.042707","article-title":"Dynamical Maximum Entropy Approach to Flocking","volume":"89","author":"Cavagna","year":"2014","journal-title":"Phys. Rev. E"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Chliamovitch, G., Dupuis, A., Golub, A., and Chopard, B. (2015). Improving Predictability of Time Series Using Maximum Entropy Methods. Europhys. Lett., 110.","DOI":"10.1209\/0295-5075\/110\/10003"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1002\/j.1538-7305.1948.tb01338.x","article-title":"A Mathematical Theory of Communication","volume":"27","author":"Shannon","year":"1948","journal-title":"Bell Syst. Tech. J."},{"key":"ref_14","unstructured":"Khinchin, A.Y. (1957). Mathematical Foundations of Information Theory, Dover."},{"key":"ref_15","unstructured":"Kreuzer, H.J. (1984). Nonequilibrium Thermodynamics and its Statistical Foundations, Oxford University Press."},{"key":"ref_16","unstructured":"Liboff, R.L. (2003). Kinetic Theory, Springer."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/17\/11\/7522\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T20:51:14Z","timestamp":1760215874000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/17\/11\/7522"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,10,30]]},"references-count":16,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2015,11]]}},"alternative-id":["e17117522"],"URL":"https:\/\/doi.org\/10.3390\/e17117522","relation":{},"ISSN":["1099-4300"],"issn-type":[{"type":"electronic","value":"1099-4300"}],"subject":[],"published":{"date-parts":[[2015,10,30]]}}}