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001454927 001__ 1454927
001454927 003__ OCoLC
001454927 005__ 20230314003235.0
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001454927 008__ 230301s2022\\\\sz\\\\\\ob\\\\001\0\eng\d
001454927 020__ $$a9783031177859$$q(electronic bk.)
001454927 020__ $$a3031177851$$q(electronic bk.)
001454927 020__ $$z9783031177842
001454927 020__ $$z3031177843
001454927 0247_ $$a10.1007/978-3-031-17785-9$$2doi
001454927 035__ $$aSP(OCoLC)1371438050
001454927 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE
001454927 049__ $$aISEA
001454927 050_4 $$aQA273
001454927 08204 $$a519.2$$223/eng/20230301
001454927 1001_ $$aCursi, Eduardo Souza de,$$eauthor.$$1https://isni.org/isni/0000000077806134
001454927 24510 $$aUncertainty quantification using R /$$cEduardo Souza de Cursi.
001454927 264_1 $$aCham :$$bSpringer,$$c2022.
001454927 300__ $$a1 online resource (1 volume)
001454927 336__ $$atext$$btxt$$2rdacontent
001454927 337__ $$acomputer$$bc$$2rdamedia
001454927 338__ $$aonline resource$$bcr$$2rdacarrier
001454927 4901_ $$aInternational series in operations research & management science ;$$vvolume 335
001454927 504__ $$aIncludes bibliographical references and index.
001454927 5050_ $$a1. Introduction -- 2. Some tips to use R and RStudio -- 3. Probabilities and Random Variables -- 4. Representation of random variables -- 5. Stochastic processes -- 6. Uncertain Algebraic Equations -- 7. Random Differential Equations -- 8. UQ in Game Theory -- 9. Optimization under uncertainty -- 10. Reliability.
001454927 506__ $$aAccess limited to authorized users.
001454927 520__ $$aThis book is a rigorous but practical presentation of the techniques of uncertainty quantification, with applications in R and Python. This volume includes mathematical arguments at the level necessary to make the presentation rigorous and the assumptions clearly established, while maintaining a focus on practical applications of uncertainty quantification methods. Practical aspects of applied probability are also discussed, making the content accessible to students. The introduction of R and Python allows the reader to solve more complex problems involving a more significant number of variables. Users will be able to use examples laid out in the text to solve medium-sized problems. The list of topics covered in this volume includes linear and nonlinear programming, Lagrange multipliers (for sensitivity), multi-objective optimization, game theory, as well as linear algebraic equations, and probability and statistics. Blending theoretical rigor and practical applications, this volume will be of interest to professionals, researchers, graduate and undergraduate students interested in the use of uncertainty quantification techniques within the framework of operations research and mathematical programming, for applications in management and planning. .
001454927 588__ $$aDescription based on print version record.
001454927 650_0 $$aUncertainty$$xMathematical models.
001454927 650_0 $$aPredicate calculus.
001454927 650_0 $$aR (Computer program language)
001454927 655_0 $$aElectronic books.
001454927 77608 $$iPrint version:$$aCursi, Eduardo Souza de.$$tUncertainty quantification using R.$$dCham : Springer, 2022$$z9783031177842$$w(OCoLC)1348393207
001454927 830_0 $$aInternational series in operations research & management science ;$$v335.
001454927 852__ $$bebk
001454927 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-17785-9$$zOnline Access$$91397441.1
001454927 909CO $$ooai:library.usi.edu:1454927$$pGLOBAL_SET
001454927 980__ $$aBIB
001454927 980__ $$aEBOOK
001454927 982__ $$aEbook
001454927 983__ $$aOnline
001454927 994__ $$a92$$bISE