Proceedings of the Pacific Rim Statistical Conference for Production Engineering: Big Data, Production Engineering and Statistics: ICSA Book Series in Statistics
Editat de Dongseok Choi, Daeheung Jang, Tze Leung Lai, Youngjo Lee, Ying Lu, Jun Ni, Peter Qian, Peihua Qiu, George Tiaoen Limba Engleză Paperback – 22 dec 2018
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Specificații
ISBN-13: 9789811340840
ISBN-10: 9811340846
Ilustrații: VIII, 170 p. 36 illus., 29 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.26 kg
Ediția:Softcover reprint of the original 1st ed. 2018
Editura: Springer Nature Singapore
Colecția Springer
Seria ICSA Book Series in Statistics
Locul publicării:Singapore, Singapore
ISBN-10: 9811340846
Ilustrații: VIII, 170 p. 36 illus., 29 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.26 kg
Ediția:Softcover reprint of the original 1st ed. 2018
Editura: Springer Nature Singapore
Colecția Springer
Seria ICSA Book Series in Statistics
Locul publicării:Singapore, Singapore
Cuprins
Section 1: Design and Collection of Big Data.- Section 2: Analytic Methods of Big Data.- Section 3: Operation/Production Decision Making.- Section 4 Reliability and Health Management.-Section 5: Statistical Theories Related with the Big Data.- Section 6: Recent Advances in Statistical Methods.- Section 7: Development of Statistical Software for the Big Data.- Section 8: Big Data and Its Application to Industry.- Section 9: Big Data and Its Application to Industry.
Textul de pe ultima copertă
This book presents the proceedings of the 2nd Pacific Rim Statistical Conference for Production Engineering: Production Engineering, Big Data and Statistics, which took place at Seoul National University in Seoul, Korea in December, 2016. The papers included discuss a wide range of statistical challenges, methods and applications for big data in production engineering, and introduce recent advances in relevant statistical methods.
Caracteristici
Presents current statistical challenges in production engineering with big data Combines research from both statistics and production engineering Presents a wide range of statistical methods and applications for big data in production engineering