03675nam a22006135i 4500 978-3-031-48208-3 DE-He213 20251030112029.0 cr nn 008mamaa 240506s2024 sz | s |||| 0|eng d 9783031482083 978-3-031-48208-3 10.1007/978-3-031-48208-3 doi T57.6-.97 KJT bicssc KJMD bicssc BUS049000 bisacsh KJT thema KJMD thema Souza de Cursi, Eduardo. author. aut http://id.loc.gov/vocabulary/relators/aut 76435 Uncertainty Quantification with R [electronic resource] : Bayesian Methods / by Eduardo Souza de Cursi. 1st ed. 2024. Cham : Springer Nature Switzerland : Imprint: Springer, 2024. VIII, 486 p. 111 illus. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda International Series in Operations Research & Management Science, 2214-7934 ; 352 Introduction- 1 -- Basic Bayesian Probabilities-2 -- Beliefs-3 -- Information and Entropy-4 -- Maximum of Entropy-5 -- Bayesian Inference-6 -- Sequential Bayesian Estimation. This 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. Operations research. 57943 Production management. 67141 Probabilities. 65724 Mathematical optimization. 65522 Statistics . 65982 Operations Research and Decision Theory. 67143 Operations Management. 67304 Applied Probability. 65727 Discrete Optimization. 73079 Bayesian Inference. 76436 SpringerLink (Online service) Springer Nature eBook Printed edition: 9783031482076 Printed edition: 9783031482090 Printed edition: 9783031482106 International Series in Operations Research & Management Science, 2214-7934 ; 352 69480 https://doi.org/10.1007/978-3-031-48208-3 ZDB-2-BUM ZDB-2-SXBM 59672 59672