出版社: Athena Scientific
出版年: 2008-7-15
页数: 544
定价: USD 91.00
装帧: Hardcover
ISBN: 9781886529236
内容简介 · · · · · ·
An intuitive, yet precise introduction to probability theory, stochastic processes, and probabilistic models used in science, engineering, economics, and related fields. The 2nd edition is a substantial revision of the 1st edition, involving a reorganization of old material and the addition of new material. The length of the book has increased by about 25 percent. The main new ...
An intuitive, yet precise introduction to probability theory, stochastic processes, and probabilistic models used in science, engineering, economics, and related fields. The 2nd edition is a substantial revision of the 1st edition, involving a reorganization of old material and the addition of new material. The length of the book has increased by about 25 percent. The main new feature of the 2nd edition is thorough introduction to Bayesian and classical statistics.
The book is the currently used textbook for "Probabilistic Systems Analysis," an introductory probability course at the Massachusetts Institute of Technology, attended by a large number of undergraduate and graduate students. The book covers the fundamentals of probability theory (probabilistic models, discrete and continuous random variables, multiple random variables, and limit theorems), which are typically part of a first course on the subject, as well as the fundamental concepts and methods of statistical inference, both Bayesian and classical. It also contains, a number of more advanced topics, from which an instructor can choose to match the goals of a particular course. These topics include transforms, sums of random variables, a fairly detailed introduction to Bernoulli, Poisson, and Markov processes.
The book strikes a balance between simplicity in exposition and sophistication in analytical reasoning. Some of the more mathematically rigorous analysis has been just intuitively explained in the text, but is developed in detail (at the level of advanced calculus) in the numerous solved theoretical problems.
Written by two professors of the Department of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology, and members of the prestigious US National Academy of Engineering, the book has been widely adopted for classroom use in introductory probability courses within the USA and abroad.
From a Review of the 1st Edition:
...it trains the intuition to acquire probabilistic feeling. This book explains every single concept it enunciates. This is its main strength, deep explanation, and not just examples that happen to explain. Bertsekas and Tsitsiklis leave nothing to chance. The probability to misinterpret a concept or not understand it is just... zero. Numerous examples, figures, and end-of-chapter problems strengthen the understanding. Also of invaluable help is the book's web site, where solutions to the problems can be found-as well as much more information pertaining to probability, and also more problem sets. --Vladimir Botchev, Analog Dialogue
Several other reviews can be found in the listing of the first edition of this book. Contents, preface, and more info at publisher's website (Athena Scientific, athenasc com)
作者简介 · · · · · ·
The authors are Professors in the Department of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology. They are members of the prestigious US National Academy of Engineering. They have written several widely used textbooks and research monographs, both individually and jointly.
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《概率导论》与《统计学》读后感
令人蛋疼的翻译:郑忠国、童行伟《概率导论》(第2版·修订版)之批评
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瞎子一般的翻译:郑忠国、童行伟《概率导论》(第2版·修订版)之批评
收到了拼多多买的概率导论。
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论坛 · · · · · ·
在这本书的论坛里发言这本书的其他版本 · · · · · · ( 全部5 )
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人民邮电出版社 (2016)9.4分 259人读过
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人民邮电出版社 (2009)9.2分 139人读过
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Athena Scientific (2002)9.5分 71人读过
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人民邮电出版社 (2022)暂无评分 8人读过
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订阅关于Introduction to Probability (2/e)的评论:
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0 有用 无 2021-08-27 00:42:04
今天想了很久的期望,在此记录想法。 1.期望=∑p*value,这是加权平均数,权重即p,value出现的概率。考虑极端的情况:一个学生考试59分的概率是99%,90分的概率是1%,那么考试得分期望其实就基本等同于只看这个59分(计算可得期望为59.31分)。 2.关于p*value联想到了赌马:瘦弱的马一旦赢了奖金很高,可是赢的概率很小;强壮的马赢的概率大,可是奖金不高。 3.实际应用中,更重要... 今天想了很久的期望,在此记录想法。 1.期望=∑p*value,这是加权平均数,权重即p,value出现的概率。考虑极端的情况:一个学生考试59分的概率是99%,90分的概率是1%,那么考试得分期望其实就基本等同于只看这个59分(计算可得期望为59.31分)。 2.关于p*value联想到了赌马:瘦弱的马一旦赢了奖金很高,可是赢的概率很小;强壮的马赢的概率大,可是奖金不高。 3.实际应用中,更重要的是根据具体的情境去设计、转化,这个过程正如数学建模,会存在信息丢失。首先问自己:是期望什么东西越高越好? 4.在一众的选择中我们要选期望高的。期望意味着随机、预测,随机意味着真实结果可能高于或低于期望,但是无妨,我们还是要选期望高的,它就像拥有高平均分的快班,即便我们清楚快班最后一名不如慢班第一。 (展开)
0 有用 吃货 2023-10-16 15:54:21 北京
很好
0 有用 混沌IPA 2019-10-09 14:09:41
第不知道多少次学概率论了,还是蛮难的……
0 有用 风油精 2020-06-08 12:02:49
MIT OCW brought life to my undergrad studying QAQ
0 有用 Jia维斯☁️ 2022-03-28 00:05:59
希腊老头太男神了!配合ocw使用
0 有用 赵建清 2024-01-06 14:46:24 北京
在MIT概率论课程基础上撰写的教程,概念阐述清晰,习题具有启发性。 读完第1-3,5章,覆盖概率论的基本概念。 完成第1-2,5章全部习题。 学习统计推断,应该继续阅读第4,8-9章。 学习随机过程,应该继续阅读第4,6-7章。
0 有用 吃货 2023-10-16 15:54:21 北京
很好
0 有用 ansondeng 2023-02-19 20:57:29 广东
70%进度,待继续
0 有用 心急的蕤靐 2022-11-30 16:55:34 浙江
不如Joseph的Introduction to Probability + George的Statistical。此书晦涩难懂,但Joseph的书有很多故事帮助形成直觉和理解。
0 有用 无语 2022-05-29 13:28:59
挺好挺好 记得做练习哟