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020 _a9783031482083
_9978-3-031-48208-3
024 7 _a10.1007/978-3-031-48208-3
_2doi
050 4 _aT57.6-.97
072 7 _aKJT
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072 7 _aBUS049000
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072 7 _aKJT
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072 7 _aKJMD
_2thema
100 1 _aSouza de Cursi, Eduardo.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_976435
245 1 0 _aUncertainty Quantification with R
_h[electronic resource] :
_bBayesian Methods /
_cby Eduardo Souza de Cursi.
250 _a1st ed. 2024.
264 1 _aCham :
_bSpringer Nature Switzerland :
_bImprint: Springer,
_c2024.
300 _aVIII, 486 p. 111 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aInternational Series in Operations Research & Management Science,
_x2214-7934 ;
_v352
505 0 _aIntroduction- 1 -- Basic Bayesian Probabilities-2 -- Beliefs-3 -- Information and Entropy-4 -- Maximum of Entropy-5 -- Bayesian Inference-6 -- Sequential Bayesian Estimation.
520 _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.
650 0 _aOperations research.
_957943
650 0 _aProduction management.
_967141
650 0 _aProbabilities.
_965724
650 0 _aMathematical optimization.
_965522
650 0 _aStatistics .
_965982
650 1 4 _aOperations Research and Decision Theory.
_967143
650 2 4 _aOperations Management.
_967304
650 2 4 _aApplied Probability.
_965727
650 2 4 _aDiscrete Optimization.
_973079
650 2 4 _aBayesian Inference.
_976436
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031482076
776 0 8 _iPrinted edition:
_z9783031482090
776 0 8 _iPrinted edition:
_z9783031482106
830 0 _aInternational Series in Operations Research & Management Science,
_x2214-7934 ;
_v352
_969480
856 4 0 _uhttps://doi.org/10.1007/978-3-031-48208-3
912 _aZDB-2-BUM
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