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010   $a9783031482083
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024 7 $a10.1007/978-3-031-48208-3
035   $a(MiAaPQ)EBC31323708
035   $a(Au-PeEL)EBL31323708
035   $a(CKB)31987995100041
035   $a(DE-He213)978-3-031-48208-3
035   $a(OCoLC)1433656762
035   $a(EXLCZ)9931987995100041
100   $a20240506d2024      u|         0
101 0 $aeng
135   $aurcnu||||||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aUncertainty Quantification with R $eBayesian Methods /$fby Eduardo Souza de Cursi
205   $a1st ed. 2024.
210  1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2024.
215   $a1 online resource (493 pages)
225 1 $aInternational Series in Operations Research & Management Science,$x2214-7934 ;$v352
311 08$a9783031482076 
311 08$a3031482077 
327   $aIntroduction- 1 -- Basic Bayesian Probabilities-2 -- Beliefs-3 -- Information and Entropy-4 -- Maximum of Entropy-5 -- Bayesian Inference-6 -- Sequential Bayesian Estimation.
330   $aThis book is a rigorous but practical presentation of the Bayesian techniques of uncertainty quantification, with applications in R. This volume includes mathematical arguments at the level necessary to make the presentation rigorous and the assumptions clearly established, while maintaining a focus on practical applications of Bayesian uncertainty quantification methods. Practical aspects of applied probability are also discussed, making the content accessible to students. The introduction of R allows the reader to solve more complex problems involving a more significant number of variables. Users will be able to use examples laid out in the text to solve medium-sized problems. The list of topics covered in this volume includes basic Bayesian probabilities, entropy, Bayesian estimation and decision, sequential Bayesian estimation, and numerical methods. Blending theoretical rigor and practical applications, this volume will be of interest to professionals, researchers, graduate and undergraduate students interested in the use of Bayesian uncertainty quantification techniques within the framework of operations research and mathematical programming, for applications in management and planning.
410  0$aInternational Series in Operations Research & Management Science,$x2214-7934 ;$v352
606   $aOperations research
606   $aProduction management
606   $aProbabilities
606   $aMathematical optimization
606   $aStatistics
606   $aOperations Research and Decision Theory
606   $aOperations Management
606   $aApplied Probability
606   $aDiscrete Optimization
606   $aBayesian Inference
615  0$aOperations research.
615  0$aProduction management.
615  0$aProbabilities.
615  0$aMathematical optimization.
615  0$aStatistics.
615 14$aOperations Research and Decision Theory.
615 24$aOperations Management.
615 24$aApplied Probability.
615 24$aDiscrete Optimization.
615 24$aBayesian Inference.
676   $a519.544
700   $aCursi$b Eduardo Souza de$0908276
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910855386803321
996   $aUncertainty Quantification with R$94464847
997   $aUNINA