Statistical approaches to randomised controlled trial analysis The statistical approach used in the design and analysis of the vast majority of clinical studies is often referred to as classical or frequentist. Conclusions are made on the results of…

3. Bayesian statistics and Regularization Content 3. Bayesian statistics and Regularization. 3.1 Underfitting and overfitting. 3.2 Bayesian statistics and regularization. 3.3 Optimize Cost function by regularization. 3.3.1 Regularized linear regressi…

听同事讲 Bayesian statistics: Part 2 - Bayesian inference 摘要:每天坐地铁上班是一件很辛苦的事,需要早起不说,如果早上开会又赶上地铁晚点,更是让人火烧眉毛.在城市里工作的人,很多是需要搭乘地铁上下班的,也包括同事M. 有一次M早上来得比较晚,进办公室以后就开始抱怨地铁又晚点了,而且同一周不只发生了一次.我说,作为 statistician,你就不能 predict 一下地铁会不会晚点吗?她说,"This is a very tricky prob…

听同事讲 Bayesian statistics: Part 1 - Bayesian vs. Frequentist   摘要:某一天与同事下班一同做地铁,刚到地铁站,同事遇到一熟人正从地铁站出来.俩人见面都特别高兴,聊了许久.过后我问她这人是谁,她说是她的朋友,伯克利的教授Michael Jordan.啊!原来他就是鼎鼎大名的Michael Jordan啊! 同事中牛人众多,姑且先称这位同事为M吧.M美国博士毕业后到英国剑桥又深造了几年,研究方向一直是 Bayesian statistics…

1. Bayesian statistics 一组独立同分布的数据集 X=(x1,-,xn)(xi∼p(xi|θ)),参数 θ 同时也是被另外分布定义的随机变量 θ∼p(θ|α),此时: p(X|α)=∫θp(X|θ)p(θ|α)dθ 2. 频率统计(frequentist statistics) 此时的 θ=(ψ,λ)(joint parameter,联合参数),其中 ψ 是真正的待求解的参数,λ 则是 nuisance parameter. L(ψ;X)=p(X|ψ)=∫λp(X|ψ,λ)p…

Common sense reduced to computation - Pierre-Simon, marquis de Laplace (1749–1827) Inventor of Bayesian inference 贝叶斯方法的逻辑十分接近人脑的思维:人脑的优势不是计算,在纯数值计算方面,可以说几十年前的计算器就已经超过人脑了. 人脑的核心能力在于推理,而记忆在推理中扮演了重要的角色,我们都是基于已知的常识来做出推理.贝叶斯推断也是如此,先验就是常识,在我们有了新的观测数据后,就可以…

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…

from: http://www.metacademy.org/roadmaps/rgrosse/bayesian_machine_learning Created by: Roger Grosse(http://www.cs.toronto.edu/~rgrosse/) Intended for: beginning machine learning researchers, practitioners Bayesian statistics is a branch of statistics…

本博客是基于对周志华教授所著的<机器学习>的"第7章 贝叶斯分类器"部分内容的学习笔记. 朴素贝叶斯分类器,顾名思义,是一种分类算法,且借助了贝叶斯定理.另外,它是一种生成模型(generative model),采用直接对联合概率P(x,c)建模,以获得目标概率值的方法. 目录 预备知识 先验概率与后验概率 贝叶斯定理(Bayesian Theorem) 朴素贝叶斯分类器 何为"朴素":属性条件独立性假设 分类准则 离散属性与连续属性值的分别处理 例子…

Problems[show] Classification Clustering Regression Anomaly detection Association rules Reinforcement learning Structured prediction Feature engineering Feature learning Online learning Semi-supervised learning Unsupervised learning Learning to rank…