LC8 Hypothesis testing & Computational alternatives

LC8 Hypothesis testing & Computational alternatives假设检验的定义与例子​ • 假设检验的形式化定义:假设 $$\theta$$ 是我们感兴趣的未知参数, S 是所有可能的取值集合。我们将 S 分成两个互斥的子集 $$S_0$$ 和 $$S_1$$ : ​ • $$H_0 : \theta \in S_0$$ (零假设),即我们最初...

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LC3 Random variables and discrete probability distributions

LC3 Random variables and discrete probability distributionsRandom variableFormal definition of a random variable:A function mapping the sample space to the real line X : Function that maps outcomes...

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LC 7. Confidence intervals and univariate hypothesis testing

LC 7. Confidence intervals and univariate hypothesis testingDescriptive statistics:Concerned with observed data: finding sample means, sample medians etc. Inferential statistics:Infers properties o...

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Colloquial Swedish Notes

Colloquial Swedish NotesTANGO1. Hejhttps://books.google.com.tw/books?id=_aWuDAAAQBAJ&pg=PA106&hl=zh-CN&source=gbs_toc_r&pli=1 Swedish Chinese French Japanese Hej 你好 Bonjour こん...

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Logic Inquiry Class Notes

Counterfactual Model of casualWorking with theories A theory: Placing Mother’s Influence at the Centre of Inquiry How to come up with research questions Lyaout a machanism competing explanations:...

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N2 単語

UNIT 1手まねで合図する。/用手勢打暗号 思いつき[名]灵机一动,偶然想起;主意,设想,点子 あいにく売り切れた - 不巧卖光了(不凑巧,实际情况与期望或目标不符;不合时机) おあいにく様/真不凑巧、遺憾 赤字を埋める。/填朴赤字。 能率が上がる。/效率提高。 上がる(あがる)⓪[自動1]登,爬;(地位、价格等)提高,上升;完成,结束;紧张,怯场;“吃、喝”的尊敬语;...

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coursera UNIT 1

ELEMENTARY SORT‣ rules of the gameSample sort client 1Goal. Sort any type of data. Ex 1. Sort random real numbers in ascending order. seems artificial, but stay tuned for an application 123456789...

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Fever's Notes on Open Courses

Overview Categories Course No. Name Discription Operating System 15-213 S081 6.824 Algorithms 6.046J 6.851 Princeton Coursera Database 15-445 Machine Learning C...

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S081 Labs

UtilitiesInstall xv6 on Catalina配置 S081 实验环境的时候,官网对于 macOS 的安装方法 brew install riscv-tools 等包管理无法在 Catalina 及以下的系统版本上安装成功,需要进行手动编译。编译项如下 RISC-V工具链: 包括一系列交叉编译的工具,用于把源码编译成机器码,如 gcc,binutils,glibc 等 Q...

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CS229 Machine Learning Notes

Intro本课程广泛介绍机器学习和统计模式识别。主题包括:监督学习(生成/判别学习、参数化/非参数化学习、神经网络、支持向量机);无监督学习(聚类、维度缩减、核方法);学习理论(偏差/方差权衡,实用建议);强化学习和自适应控制。本课程还将讨论机器学习的最新应用,如机器人控制、数据挖掘、自主导航、生物信息学、语音识别以及文本和网页数据处理。 Res 2018 年秋...

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