{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T07:43:39Z","timestamp":1790840619798,"version":"4.1.0"},"reference-count":51,"publisher":"Society for Industrial & Applied Mathematics (SIAM)","issue":"4","funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["DMS-1522765"],"award-info":[{"award-number":["DMS-1522765"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["SIAM J. Sci. Comput."],"published-print":{"date-parts":[[2017,1]]},"abstract":"<jats:p>Stochastic models are necessary for the realistic description of an increasing number of applications. The ability to identify influential parameters and variables is critical to a thorough analysis and understanding of the underlying phenomena. We present a new global sensitivity analysis approach for stochastic models, i.e., models with both uncertain parameters and intrinsic stochasticity. Our method relies on an analysis of variance through a generalization of Sobol' indices and on the use of surrogate models. We show how to efficiently compute the statistical properties of the resulting indices and illustrate the effectiveness of our approach by computing first order Sobol' indices for two stochastic models.<\/jats:p>","DOI":"10.1137\/16m106193x","type":"journal-article","created":{"date-parts":[[2017,8,17]],"date-time":"2017-08-17T15:26:15Z","timestamp":1502983575000},"page":"A1514-A1530","source":"Crossref","is-referenced-by-count":47,"title":["Efficient Computation of Sobol' Indices for Stochastic Models"],"prefix":"10.1137","volume":"39","author":[{"given":"J. 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