TY  - GEN
N2  - This volume presents a selection of articles on statistical modeling and simulation, with a focus on different aspects of statistical estimation and testing problems, the design of experiments, reliability and queueing theory, inventory analysis, and the interplay between statistical inference, machine learning methods and related applications. The refereed contributions originate from the 10th International Workshop on Simulation and Statistics, SimStat 2019, which was held in Salzburg, Austria, September 26, 2019, and were either presented at the conference or developed afterwards, relating closely to the topics of the workshop. The book is intended for statisticians and Ph.D. students who seek current developments and applications in the field.
DO  - 10.1007/978-3-031-40055-1
DO  - doi
AB  - This volume presents a selection of articles on statistical modeling and simulation, with a focus on different aspects of statistical estimation and testing problems, the design of experiments, reliability and queueing theory, inventory analysis, and the interplay between statistical inference, machine learning methods and related applications. The refereed contributions originate from the 10th International Workshop on Simulation and Statistics, SimStat 2019, which was held in Salzburg, Austria, September 26, 2019, and were either presented at the conference or developed afterwards, relating closely to the topics of the workshop. The book is intended for statisticians and Ph.D. students who seek current developments and applications in the field.
T1  - Statistical modeling and simulation for experimental design and machine learning applications :selected contributions from SimStat 2019 and invited papers /
AU  - Pilz, Jürgen,
AU  - Melas, V. B.
AU  - Bathke, Arne,
CN  - QA270
ID  - 1482639
KW  - Plan d'expérience
KW  - Apprentissage automatique
KW  - Experimental design
KW  - Machine learning
SN  - 9783031400551
SN  - 3031400550
TI  - Statistical modeling and simulation for experimental design and machine learning applications :selected contributions from SimStat 2019 and invited papers /
LK  - https://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-40055-1
UR  - https://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-40055-1
ER  -