{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T05:57:09Z","timestamp":1775541429612,"version":"3.50.1"},"reference-count":7,"publisher":"Walter de Gruyter GmbH","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016,6,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>The paper is devoted to the complexity analysis of binary floating point pseudorandom generators. We start with a stochastic model of a \u201cusual\u201d\npseudorandom generator (PRNG). Then integer outputs\nof this generator are transformed into i.i.d. random variables,\nagreed with\nan abstract binary floating point system. Additionally, these random variables\nare approximately uniformly distributed on the interval [0,1]. Therefore, they can interpreted as (random) outputs of a binary floating point pseudorandom generator\n(flPRNG).\nThe simulation complexity of such a transformation is defined as the average number of PRNG's outputs\nnecessary to produce the unique\noutput of flPRNG.\nSeveral transformations with minimal or approximately minimal complexities are presented and discussed.<\/jats:p>","DOI":"10.1515\/mcma-2016-0105","type":"journal-article","created":{"date-parts":[[2016,6,2]],"date-time":"2016-06-02T10:01:28Z","timestamp":1464861688000},"page":"109-116","source":"Crossref","is-referenced-by-count":2,"title":["On the complexity of binary floating point pseudorandom generation"],"prefix":"10.1515","volume":"22","author":[{"given":"Vladimir","family":"Nekrutkin","sequence":"first","affiliation":[{"name":"St.Petersburg State University, 7\/9 Universitetskaya nab., St. Petersburg, 199034, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"374","published-online":{"date-parts":[[2016,6,1]]},"reference":[{"key":"2023040101305240478_j_mcma-2016-0105_ref_000_w2aab2b8b4b1b7b1ab1ab1Aa","unstructured":"A. Fog,\nPseudo random number generators,\nhttp:\/\/www.agner.org\/random\/."},{"key":"2023040101305240478_j_mcma-2016-0105_ref_001_w2aab2b8b4b1b7b1ab1ab2Aa","doi-asserted-by":"crossref","unstructured":"T. S. Han and M. Hoshi,\nInterval algorithm for random number generation,\nIEEE Trans. Inform. Theory 43 (1997), 2, 59\u2013611.","DOI":"10.1109\/18.556116"},{"key":"2023040101305240478_j_mcma-2016-0105_ref_002_w2aab2b8b4b1b7b1ab1ab3Aa","unstructured":"D. Knuth and A. Yao,\nThe complexity of nonuniform random number generation,\nAlgorithms and Complexity,\nAcademic Press, New York (1976), 357\u2013428."},{"key":"2023040101305240478_j_mcma-2016-0105_ref_003_w2aab2b8b4b1b7b1ab1ab4Aa","doi-asserted-by":"crossref","unstructured":"D. Romik,\nSharp entropy bounds for discrete statistical simulation,\nStatist. Probab. Lett. 42 (1999), 219\u2013227.","DOI":"10.1016\/S0167-7152(98)00174-6"},{"key":"2023040101305240478_j_mcma-2016-0105_ref_004_w2aab2b8b4b1b7b1ab1ab5Aa","doi-asserted-by":"crossref","unstructured":"M. Saito and M. Matsumoto,\nA PRNG specialized in double precision floating point numbers using an affine transition,\nMonte Carlo and Quasi-Monte Carlo Methods 2008,\nSpringer, Berlin (2009), 589\u2013602.","DOI":"10.1007\/978-3-642-04107-5_38"},{"key":"2023040101305240478_j_mcma-2016-0105_ref_005_w2aab2b8b4b1b7b1ab1ab6Aa","unstructured":"N. Vorobjeva, A. Korobeinikov and V. Nekrutkin,\nOptimal simulation of discrete distributions (in Russian),\nVestnik St. Petersburg Univ. Math. 1 (2012), 3, 14\u201323."},{"key":"2023040101305240478_j_mcma-2016-0105_ref_006_w2aab2b8b4b1b7b1ab1ab7Aa","unstructured":"IEEE Standard for floating-foint arithmetic, IEEE Std 754-2008."}],"container-title":["Monte Carlo Methods and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/mcma-2016-0105\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/mcma-2016-0105\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,1]],"date-time":"2023-04-01T20:24:44Z","timestamp":1680380684000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/mcma-2016-0105\/html"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,6,1]]},"references-count":7,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2016,6,1]]},"published-print":{"date-parts":[[2016,6,1]]}},"alternative-id":["10.1515\/mcma-2016-0105"],"URL":"https:\/\/doi.org\/10.1515\/mcma-2016-0105","relation":{},"ISSN":["0929-9629","1569-3961"],"issn-type":[{"value":"0929-9629","type":"print"},{"value":"1569-3961","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,6,1]]}}}