All of Statistics 作者: Larry Wasserman 出版社: Springer副标题: A Concise Course in Statistical Inference出版年: 2004-10-21页数: 442定价: USD 99.00装帧: Hardcover丛书: Springer Texts in StatisticsISBN: 9780387402727豆瓣评分 9.1 258人评价 5星 62.4% 4星 28.7% 3星 8.5% 2星 0.0% 1星 0.4% ...
统计学全书wasserman,allofstatistics:a concisecourseinstatisticalinference LarryWasserman AllofNonparametric Statistics With52Illustrations Preface Takenliterally,thetitle“AllofStatistics”isan exaggeration.Butinspirit, thetitleisapt,asthebookdoescoveramuchbroaderrangeoftopics ...
悲剧 http://www.gobookee.org/all-of-statistics-wasserman-solutions/ 貌似是答案,但注册下载需要visa信用卡。如果哪位大侠下载了,求发我份,万谢! 2013-11-17 08:25:433人喜欢 展开 第1页 wilcoxon test porco 2013-12-17 16:42:57 展开 第1页 The Kruskal-Wallis Test...
作者:Larry Wasserman(L. 沃塞曼 / 拉里· 沃瑟曼)著 出版社:Springer 出版时间:2010-00-00 印刷时间:0000-00-00 ISBN:9781441923226 ,购买All of Statistics: A Concise Course in Statistical Inference 统计学完全教程 1441923225等理科工程技术相关商品,欢迎您
All of Statistics Solutions toWasserman's 'All Of Statistics', using the 2005 corrected second printing, ISBN 0-387-40272-1. Organisation Please follow these guidelines: Solutions go insolutionsas markdown or notebook (ipynb). They should be labelledchXXqYYwhereXXis the (0 padded) chapter numb...
Larry Wasserman. All of Statistics. Springer, 2003.L. Wasserman, All of statistics, Springer-Verlag, New York, 2004, https://doi.org/10.1007/978-0-387-21736-9.L. Wasserman, All of Statistics, Springer, New York, USA, 2010.Wasserman, L. (2004). All of statistics. Springer New York....
All of Statistics豆瓣评分:9.1 简介:WINNER OF THE 2005 DEGROOT PRIZE! This book is for people who want to learn probability and statistics quickly. It brings together many of the main ideas in modern statistics in one place. The book i
当当中华商务进口图书旗舰店在线销售正版《【中商原版】统计学完全教程 All of Statistics A Concise Course in Statistical Inference 英文原版 Larry Wasserman》。最新《【中商原版】统计学完全教程 All of Statistics A Concise Course in Statistical Inference 英文原
All of Statistics: A Concise Course in Statistical Inference by Larry Wasserman Machine Learning by Tom Mitchell Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications by Chip Huyen How would you define GAN(Generative Adversarial Networks) ? What are Gausian Processes...
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