C_OperatingSystem_Experiments：操作系统作业。 :winking_face:

在Kubernetes上发火花 此回购协议已分解为几个概念证明： （项目报告以法语在Alain April网站上提供： : ） 概念证明 Kubernetes运营（kops） 此概念证明旨在在具有多个ec2实例的Amazon AWS上创建Kubernetes集群。 使用的工具是kops，它允许自动设置和配置ec2实例以形成Kubernetes集群。 详细信息位于 在该目录中，我们具有由kops创建的Kubernetes集群的yaml配置。 理想情况下，我们应该使用该配置创建集群，但是现在，该集群是通过执行bash脚本setup-aws.sh创建的 您可以更改在文件顶部定义的脚本编辑环境变量的配置值。 请注意，kops使用S3备份集群状态。 以下是用于在AWS上创建或删除集群的选项： # Creating the cluster on AWS ./setup-aws.sh --create # Removing the cluster on AWS (including the S3 backup storage) ./setup-aws.sh --delete 以下

由Cleve Moler，MathWorks董事长和首席数学家，所编写的MATLAB实验，附程序压缩包

回归分析与实验设计 design and analysis of experiments

The Design and Analysis of Computer Experiments (Springer Series in Statistics) By 作者: Thomas J. Santner – Brian J. Williams – William I. Notz ISBN-10 书号: 149398845X ISBN-13 书号: 9781493988457 Edition 版本: 2nd ed. 2018 出版日期: 2019-01-09 pages 页数: (436 ) $119.99 This book describes methods for designing and analyzing experiments that are conducted using a computer code, a computer experiment, and, when possible, a physical experiment. Computer experiments continue to increase in popularity as surrogates for and adjuncts to physical experiments. Since the publication of the first edition, there have been many methodological advances and software developments to implement these new methodologies. The computer experiments literature has emphasized the construction of algorithms for various data analysis tasks (design construction, prediction, sensitivity analysis, calibration among others), and the development of web-based repositories of designs for immediate application. While it is written at a level that is accessible to readers with Masters-level training in Statistics, the book is written in sufficient detail to be useful for practitioners and researchers. New to this revised and expanded edition: An expanded presentation of basic material on computer experiments and Gaussian processes with additional simulations and examples A new comparison of plug-in prediction methodologies for real-valued simulator output An enlarged discussion of space-filling designs including Latin Hypercube designs (LHDs), near-orthogonal designs, and nonrectangular regions A chapter length description of process-based designs for optimization, to improve good overall fit, quantile estimation, and Pareto optimization A new chapter describing graphical and numerical sensitivity analysis tools Substantial new material on calibration-based prediction and inference for calibration parameters Lists of software that can be used to fit models discussed in the book to aid practitioners