{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,4,1]],"date-time":"2023-04-01T23:40:23Z","timestamp":1680392423350},"reference-count":16,"publisher":"Walter de Gruyter GmbH","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016,9,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Generalized Weighted Analog Sampling is a variance-reducing method for\nsolving radiative transport problems that makes use of a biased (though\nasymptotically unbiased) estimator. The introduction of bias provides a\nmechanism for combining the best features of unbiased estimators while\navoiding their limitations. In this paper we present a new proof that\nadaptive GWAS estimation based on combining the variance-reducing power of\nimportance sampling with the sampling simplicity of correlated sampling\nyields geometrically convergent estimates of radiative transport solutions.\nThe new proof establishes a stronger and more general theory of geometric\nconvergence for GWAS.<\/jats:p>","DOI":"10.1515\/mcma-2016-0110","type":"journal-article","created":{"date-parts":[[2016,6,30]],"date-time":"2016-06-30T10:02:40Z","timestamp":1467280960000},"page":"161-196","source":"Crossref","is-referenced-by-count":1,"title":["A new proof of geometric convergence for the adaptive generalized weighted analog sampling (GWAS) method"],"prefix":"10.1515","volume":"22","author":[{"given":"Rong","family":"Kong","sequence":"first","affiliation":[{"name":"Hyundai Capital America, 3161 Michelson Drive, Suite 1900, Irvine, CA 92612, United States of America"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jerome","family":"Spanier","sequence":"additional","affiliation":[{"name":"Beckman Laser Institute and Medical Clinic, 1002 Health Science Road E., University of California, Irvine, California 92612, United States of America"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"374","published-online":{"date-parts":[[2016,6,30]]},"reference":[{"key":"2023040101595005882_j_mcma-2016-0110_ref_001_w2aab2b8e1254b1b7b1ab2b1b1Aa","unstructured":"Booth T.,\nExponential convergence for Monte Carlo particle transport,\nTrans. Amer. Nuclear Soc. 50 (1985), 267\u2013268."},{"key":"2023040101595005882_j_mcma-2016-0110_ref_002_w2aab2b8e1254b1b7b1ab2b1b2Aa","doi-asserted-by":"crossref","unstructured":"Booth T.,\nZero-variance solutions for linear Monte Carlo,\nNuclear Sci. Eng. 102 (1989), 332\u2013340.","DOI":"10.13182\/NSE89-A23646"},{"key":"2023040101595005882_j_mcma-2016-0110_ref_003_w2aab2b8e1254b1b7b1ab2b1b3Aa","doi-asserted-by":"crossref","unstructured":"Booth T.,\nExponential convergence on a continuous Monte Carlo transport problem,\nNuclear Sci. Eng. 127 (1997), 338\u2013345.","DOI":"10.13182\/NSE97-A1939"},{"key":"2023040101595005882_j_mcma-2016-0110_ref_004_w2aab2b8e1254b1b7b1ab2b1b4Aa","unstructured":"Case K. M. and Zweifel P. W.,\nLinear Transport Theory,\nAddison-Wesley, Reading, 1967."},{"key":"2023040101595005882_j_mcma-2016-0110_ref_005_w2aab2b8e1254b1b7b1ab2b1b5Aa","doi-asserted-by":"crossref","unstructured":"Kong R. and Spanier J.,\nError analysis of sequential monte carlo methods for transport problems,\nMonte Carlo and Quasi-Monte Carlo Methods 1998 (Claremont 1998),\nSpringer, Berlin (2000), 252\u2013272.","DOI":"10.1007\/978-3-642-59657-5_17"},{"key":"2023040101595005882_j_mcma-2016-0110_ref_006_w2aab2b8e1254b1b7b1ab2b1b6Aa","doi-asserted-by":"crossref","unstructured":"Kong R. and Spanier J.,\nSequential correlated sampling methods for some transport problems,\nMonte Carlo and Quasi-Monte Carlo Methods 1998 (Claremont 1998),\nSpringer, Berlin (2000), 238\u2013251.","DOI":"10.1007\/978-3-642-59657-5_16"},{"key":"2023040101595005882_j_mcma-2016-0110_ref_007_w2aab2b8e1254b1b7b1ab2b1b7Aa","doi-asserted-by":"crossref","unstructured":"Kong R. and Spanier J.,\nResidual versus error in transport problems,\nMonte Carlo and Quasi-Monte Carlo Methods 2000 (Hong Kong 2000),\nSpringer, Berlin (2002), 306\u2013317.","DOI":"10.1007\/978-3-642-56046-0_20"},{"key":"2023040101595005882_j_mcma-2016-0110_ref_008_w2aab2b8e1254b1b7b1ab2b1b8Aa","doi-asserted-by":"crossref","unstructured":"Kong R. and Spanier J.,\nA new proof of geometric convergence for general transport problems based on sequential correlated sampling methods,\nJ. Comput. Physics 227 (2008), 9762\u20139777.","DOI":"10.1016\/j.jcp.2008.07.016"},{"key":"2023040101595005882_j_mcma-2016-0110_ref_009_w2aab2b8e1254b1b7b1ab2b1b9Aa","doi-asserted-by":"crossref","unstructured":"Kong R. and Spanier J.,\nGeometric convergence of adaptive monte carlo algorithms for radiative transport problems based on importance sampling methods,\nNuclear Sci. Eng. 168 (2011), 197\u2013225.","DOI":"10.13182\/NSE10-29"},{"key":"2023040101595005882_j_mcma-2016-0110_ref_010_w2aab2b8e1254b1b7b1ab2b1c10Aa","doi-asserted-by":"crossref","unstructured":"Lai Y. and Spanier J.,\nAdaptive importance sampling algorithms for transport problems,\nMonte Carlo and Quasi-Monte Carlo Methods 1998 (Claremont 1998),\nSpringer, Berlin (2000), 273\u2013283.","DOI":"10.1007\/978-3-642-59657-5_18"},{"key":"2023040101595005882_j_mcma-2016-0110_ref_011_w2aab2b8e1254b1b7b1ab2b1c11Aa","doi-asserted-by":"crossref","unstructured":"Powell M. J. 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W.,\nDensity Estimation for Statistics and Data Analysis,\nChapman and Hall, London, 1986."},{"key":"2023040101595005882_j_mcma-2016-0110_ref_016_w2aab2b8e1254b1b7b1ab2b1c16Aa","unstructured":"Advanced Monte Carlo Methods,\nCRIAMS report LANL-03-001 to Los Alamos National Laboratory, February, 2003."}],"container-title":["Monte Carlo Methods and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/mcma-2016-0110\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/mcma-2016-0110\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,1]],"date-time":"2023-04-01T23:04:45Z","timestamp":1680390285000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/mcma-2016-0110\/html"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,6,30]]},"references-count":16,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2016,9,1]]},"published-print":{"date-parts":[[2016,9,1]]}},"alternative-id":["10.1515\/mcma-2016-0110"],"URL":"https:\/\/doi.org\/10.1515\/mcma-2016-0110","relation":{},"ISSN":["0929-9629","1569-3961"],"issn-type":[{"value":"0929-9629","type":"print"},{"value":"1569-3961","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,6,30]]}}}