From: https://alexanderetz.com/2015/04/15/understanding-bayes-a-look-at-the-likelihood/ Reading note. Much of the discussion in psychology surrounding Bayesian inference focuses on priors. Should we embrace priors, or should we be skeptical? When are…

From: https://alexanderetz.com/2015/08/09/understanding-bayes-visualization-of-bf/ Nearly被贝叶斯因子搞死,找篇神文舔. In the first post of the Understanding Bayes series I said: The likelihood is the workhorse of Bayesian inference. In order to understand Bayesia…

From: https://alexanderetz.com/2015/07/25/understanding-bayes-updating-priors-via-the-likelihood/ Reading note. In a previous post I outlined the basic idea behind likelihoods and likelihood ratios. Likelihoods are relatively straightforward to under…

Relevant Readable Links Name Interesting topic Comment Edwin Chen 非参贝叶斯   徐亦达老板 Dirichlet Process 学习目标:Dirichlet Process, HDP, HDP-HMM, IBP, CRM Alex Kendall Geometry and Uncertainty in Deep Learning for Computer Vision 语义分割 colah's blog Feature Visu…

Mahout Bayes分类器是按照<Tackling the Poor Assumptions of Naive Bayes Text Classiers>论文写出来了,具体查看论文 实现包括三部分:The Trainer(训练器).The Model(模型).The Classifier(分类器) 1.训练 首先,要对输入数据进行预处理,转化成Bayes M/R job读入数据要求的格式,即训练器输入的数据是KeyValueTextInputFormat格式,第一个字符是类标签,剩余的是特…

第4章 基于概率论的分类方法:朴素贝叶斯 朴素贝叶斯 概述 贝叶斯分类是一类分类算法的总称,这类算法均以贝叶斯定理为基础,故统称为贝叶斯分类.本章首先介绍贝叶斯分类算法的基础——贝叶斯定理.最后,我们通过实例来讨论贝叶斯分类的中最简单的一种: 朴素贝叶斯分类. 贝叶斯理论 & 条件概率 贝叶斯理论 我们现在有一个数据集,它由两类数据组成,数据分布如下图所示: 我们现在用 p1(x,y) 表示数据点 (x,y) 属于类别 1(图中用圆点表示的类别)的概率,用 p2(x,y) 表示数据点 (x,y)…

The Basics of Probability Probability measures the amount of uncertainty of an event: a fact whose occurence is uncertain. Sample space refers to the set of all possible events, denoted as . Some properties: Sum rule: Union bound: Conditional probabi…

编辑 | MingMing 尽管机器学习的历史可以追溯到1959年,但目前,这个领域正以前所未有的速度发展.最近,我一直在网上寻找关于机器学习和NLP各方面的好资源,为了帮助到和我有相同需求的人,我整理了一份迄今为止我发现的最好的教程内容列表. 通过教程中的简介内容讲述一个概念.避免了包括书籍章节涵盖范围广,以及研究论文在教学理念上做的不好的特点. 我把这篇文章分成四个部分:机器学习.NLP.Python和数学. 每个部分中都包含了一些主题文章,但是由于材料巨大,每个部分不可能包含所有可能的主题…

I joined Analytics Vidhya as an intern last summer. I had no clue what was in store for me. I had been following the blog for some time and liked the community, but did not know what to expect as an intern. The initial few days were good – all the in…

文件夹 1Bayesian model selection贝叶斯模型选择 1奥卡姆剃刀Occams razor原理 2Computing the marginal likelihood evidence 2-1 BIC approximation to log marginal likelihood 2-2贝叶斯因子 3先验 3-1 确定无信息先验分布的Jeffreys原则 3-2共轭先验Conjugate Priors 4Hierarchical Bayes 5Empirical Bayes…