LEADER 03659nam 22006015 450 001 9910674355303321 005 20251008133618.0 010 $a9783031177859$b(electronic bk.) 010 $z9783031177842 024 7 $a10.1007/978-3-031-17785-9 035 $a(MiAaPQ)EBC7206700 035 $a(Au-PeEL)EBL7206700 035 $a(CKB)26183513400041 035 $a(DE-He213)978-3-031-17785-9 035 $a(PPN)268204845 035 $a(EXLCZ)9926183513400041 100 $a20230222d2023 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aUncertainty Quantification using R /$fby Eduardo Souza de Cursi 205 $a1st ed. 2023. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2023. 215 $a1 online resource (768 pages) 225 1 $aInternational Series in Operations Research & Management Science,$x2214-7934 ;$v335 311 08$aPrint version: Souza de Cursi, Eduardo Uncertainty Quantification Using R Cham : Springer International Publishing AG,c2023 9783031177842 320 $aIncludes bibliographical references and index. 327 $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. 330 $aThis book is a rigorous but practical presentation of the techniques of uncertainty quantification, with applications in R and Python. This volume includes mathematical arguments at the level necessary to make the presentation rigorous and the assumptions clearly established, while maintaining a focus on practical applications of uncertainty quantification methods. Practical aspects of applied probability are also discussed, making the content accessible to students. The introduction of R and Python allows the reader to solve more complex problems involving a more significant number of variables. Users will be able to use examples laid out in the text to solve medium-sized problems. The list of topics covered in this volume includes linear and nonlinear programming, Lagrange multipliers (for sensitivity), multi-objective optimization, game theory, as well as linear algebraic equations, and probability and statistics. Blending theoretical rigor and practical applications, this volume will be of interest to professionals, researchers, graduate and undergraduate students interested in the use of uncertainty quantification techniques within the framework of operations research and mathematical programming, for applications in management and planning. . 410 0$aInternational Series in Operations Research & Management Science,$x2214-7934 ;$v335 606 $aOperations research 606 $aProbabilities 606 $aMathematical optimization 606 $aOperations Research and Decision Theory 606 $aApplied Probability 606 $aDiscrete Optimization 615 0$aOperations research. 615 0$aProbabilities. 615 0$aMathematical optimization. 615 14$aOperations Research and Decision Theory. 615 24$aApplied Probability. 615 24$aDiscrete Optimization. 676 $a519.502855133 676 $a519.2 700 $aCursi$b Eduardo Souza de$0908276 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 912 $a9910674355303321 996 $aUncertainty quantification using R$93375232 997 $aUNINA