Statistical Modeling and Simulation for Experimental Design and Machine Learning Applications

Jürgen Pilz

ISBN 10: 3031400577 / ISBN 13: 9783031400575

Published by Springer, Springer Okt 2024, 2024

About this Item

This item is printed on demand - Print on Demand Titel. Neuware -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 2¿6, 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.Springer-Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 276 pp. Englisch.

Seller Inventory # 9783031400575

Bibliographic Details

• Title: Statistical Modeling and Simulation for Experimental Design and Machine Learning Applications
• Publisher: Springer, Springer Okt 2024
• Publication Date: 2024
• Language: English
• ISBN 10: 3031400577
• ISBN 13: 9783031400575
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 155H x 16D millimeters 235 millimeters width by 155 millimeters height by 16 millimeters depth
• Weight: 423 grams

About this title

Synopsis

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 2–6, 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.

About the Author

Jürgen Pilz is Professor Emeritus at the Department of Statistics at the Alpen-Adria University Klagenfurt in Austria. His research areas include Bayesian statistics, spatial statistics, environmental and industrial statistics, statistical quality control and design of experiments.

Viatcheslav B. Melas is a Professor at the Department of Stochastic Simulation at the St. Petersburg State University, Russia. His research areas include experimental design, stochastic simulation and regression analysis, with a focus on functional approaches to optimal experimental design.

Arne Bathke is Full Professor of Statistics at the Paris Lodron University Salzburg, Austria. His main research interests are related to nonparametric and multivariate statistics applied in different fields, from social sciences to biomedicine and engineering.