基于MATLAB的泊松过程的仿真，具有效果图。可以作为概率论和数学实验的大作业，基于MATLAB的泊松过程的仿真。 之前被系统设为10个积分，我觉得太高了，重新上传调整了积分。

基于matlab的布朗运送的仿真， 可以作为概率论和数学实验的大作业，还可以做一下简单的matlab练习，欢迎批评指正。 之前被系统设为10个积分，我觉得太高了，重新上传调整了积分。

Fourth Edition，Henry Stark Illinois Institute of Technology John W. Woods Rensselaer Polytechnic Institute

蒙特卡罗模拟理论指导，适用于数学物理学化学乃至许多工程类学科学习

中文名叫做统计学的世界，经典统计学入门书. 非常有趣的书。

IBM SPSS Statistics 20.0官方中文手册

Pratap Dangeti, "Statistics for Machine Learning" English | ISBN: 1788295757 | 2017 | EPUB | 311 pages | 12 MB Key Features Learn about the statistics behind powerful predictive models with p-value, ANOVA, F-statistics. Implement statistical computations programmatically for supervised and unsupervised learning through K-means clustering. Master the statistical aspect of machine learning with the help of this example-rich guide in R & Python. Book Description Complex statistics in machine learning worries a lot of developers. Knowing statistics helps in building strong machine learning models that are optimized for a given problem statement. This book will teach you all it takes to perform complex statistical computations required for machine learning. You will gain information on statistics behind supervised learning, unsupervised learning, reinforcement learning, and more. You will see real-world examples that discuss the statistical side of machine learning and make you comfortable with it. You will come across programs for performing tasks such as model, parameters fitting, regression, classification, density collection, working with vectors, matrices, and more.By the end of the book, you will understand concepts of required statistics for Machine Learning and will be able to apply your new skills to any sort of industry problems. What you will learn Understanding Statistical & Machine learning fundamentals necessary to build models Understanding major differences & parallels between statistics way of solving problem & machine learning way of solving problem Know how to prepare data and "feed" the models by using the appropriate machine learning algorithms from the adequate R & Python packages Analyze the results and tune the model appropriately to his or her own predictive goals Understand concepts of required statistics for Machine Learning Draw parallels between statistics and machine learning Understand each component of machine learning models and see imp

For Bayesian learning. For beginners. Easy but useful

Probability and Statistics for Computer Science 英文无水印原版pdf pdf所有页面使用FoxitReader、PDF-XChangeViewer、SumatraPDF和Firefox测试都可以打开 本资源转载自网络，如有侵权，请联系上传者或csdn删除 查看此书详细信息请在美国亚马逊官网搜索此书

Practical Statistics for Data Scientists 50 Essential Concepts 英文无水印转化版pdf pdf所有页面使用FoxitReader、PDF-XChangeViewer、SumatraPDF和Firefox测试都可以打开 本资源转载自网络，如有侵权，请联系上传者或csdn删除查看此书详细信息请在美国亚马逊官网搜索此书
Practical statistics for data scientistsby peter bruce and andrew bruceCopyright@ 2017 Peter Bruce and Andrew Bruce. All rights reservedPrinted in the united states of americaPublished by o'reilly media, InC. 1005 Gravenstein Highway North,Sebastopol, Ca95472O'Reilly books may be purchased for educational, business, or sales promotionaluse.onlineeditionsarealsoavailableformosttitles(http:/oreilly.com/safari)For more information, contact our corporate/institutional sales department: 800998-9938 or corporateoreilly com■ editor: Shannon cuttProduction editor. Kristen brownCopyeditor: Rachel monaghana Proofreader eliahu sussmana Indexer Ellen Troutman-Zaiga Interior Designer: David FutatoCover Designer: Karen Montgomeryllustrator. rebecca demaresta May 2017: First EditionRevision history for the First edition2017-05-09 First releaseSeehttp://oreilly.com/catalog/errata.csp?isbn=9781491952962forreleasedetailsThe o reilly logo is a registered trademark of o' reilly media, Inc. PracticalStatistics for Data Scientists, the cover image, and related trade dress aretrademarks of o'Reilly Media, IncWhile the publisher and the authors have used good faith efforts to ensure that theinformation and instructions contained in this work are accurate, the publisher andthe authors disclaim all responsibility for errors or omissions, including withoutlimitation responsibility for damages resulting from the use of or reliance on thiswork. Use of the information and instructions contained in this work is at yourown risk. If any code samples or other technology this work contains or describesis subject to open source licenses or the intellectual property rights of others, it isyour responsibility to ensure that your use thereof complies with such licensesand/or rights978-1-491-95296-2DedicationWe would like to dedicate this book to the memories of our parents Victor gBruce and Nancy C. bruce, who cultivated a passion for math and science and toour early mentors John W. Tukey and Julian Simon, and our lifelong friend GeoffWatson, who helped inspire us to pursue a career in statisticsPrefaceThis book is aimed at the data scientist with some familiarity with the rprogramming language, and with some prior(perhaps spotty or ephemeral)exposure to statistics. Both of us came to the world of data science from the worldof statistics, so we have some appreciation of the contribution that statistics canmake to the art of data science. at the same time we are well aware of thelimitations of traditional statistics instruction: statistics as a discipline is a centuryand a half old and most statistics textbooks and courses are laden with themomentum and inertia of an ocean linerTwo goals underlie this bookTo lay out, in digestible, navigable, and easily referenced form, key conceptsfrom statistics that are relevant to data scienceTo explain which concepts are important and useful from a data scienceperspective, which are less so, and whyWhat to ExpectKEY TERMSData science is a fusion of multiple disciplines, inc hiding statistics, computer science, informationtechnology, and domain-specific fields. As a result, several different terms could be used to reference aiven concept. Key terms and their synonyms will be highlighted throughout the book n a side bar such asConventions used in This bookThe following typographical conventions are used in this bookItalicIndicates new terms URls. email addresses filenames and file extensionsConstant widthUsed for program listings, as well as within paragraphs to refer to programelements such as variable or function names, databases, data types,environment variables, statements, and keywordsConstant width boldShows commands or other text that should be typed literally by the userConstant width italicShows text that should be replaced with user-supplied values or by valuesdetermined by contextTIPThis element signifies a tip or suggestionNOTEThis element signifies a general noteWARNINGThis element indicates a warning or cautionUsing Code ExamplesSupplemental material(code examples, exercises, etc. is available for downloadathttps://github.com/andrewgbruce/statistics-for-data-scientistsThis book is here to help you get your job done. In general, if example code isoffered with this book, you may use it in your programs and documentation. youdo not need to contact us for permission unless you're reproducing a significantportion of the code. For example, writing a program that uses several chunks ofcode from this book does not require permission. Selling or distributing a CD-ROM of examples from O Reilly books does require permission. answering aquestion by citing this book and quoting example code does not requirepermission. Incorporating a significant amount of example code from this bookinto your product's documentation does require permissionWe appreciate, but do not require, attribution. An attribution usually includes thetitle, author, publisher, and isBN. For example: Practical Statistics for dataScientists by Peter Bruce and Andrew Bruce(o'Reilly). 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