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Springer Statistical Modeling and Simulation for Experimental Design

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Description

This scholarly volume from Springer brings together refereed contributions on statistical modeling and simulation, drawing from the 10th International Workshop on Simulation and Statistics held in Salzburg in September 2019. The collection covers statistical estimation and testing, experimental design methodology, reliability theory, queueing analysis, and inventory management. Contributors explore the intersection of statistical inference with machine learning methods and their practical applications across diverse fields. Whether you work in research, academia, or applied statistics, this curated selection of SimStat 2019 papers and invited contributions provides insights into modern approaches to experimental design, simulation techniques, and the integration of classical statistics with contemporary machine learning frameworks.

Key Features

Springer-published collection of peer-reviewed articles from the SimStat 2019 International Workshop on Simulation and Statistics.

Covers statistical estimation, hypothesis testing, experimental design methodology, and reliability theory applications.

Explores the relationship between statistical inference approaches and machine learning method integration.

Includes contributions on queueing theory, inventory analysis, and simulation-based research techniques.

Features both conference presentations and post-workshop developed papers addressing current challenges in statistical modelling.

Suitable for researchers, statisticians, and practitioners working with experimental design and computational statistics.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
20 October 2023
Listed Since
29 June 2023

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