Reading Note : Parameter estimation for text analysis 暨LDA学习小结 原文:http://www.xperseverance.net/blogs/2013/03/1744/ 伟大的Parameter estimation for text analysis!当把这篇看的差不多的时候,也就到了LDA基础知识终结的时刻了,意味着LDA基础模型的基本了解完成了.所以对该模型的学习告一段落,下一阶段就是了解LDA无穷无尽的变种,不过那些不是很有用了…

点估计 Point Estimation 最大似然估计(Maximum Likelihood Estimate —— MLE):视θ为固定的参数,假设存在一个最佳的参数(或参数的真实值是存在的),目的是找到这个值. θ = argmax l(θ) 最大后验估计(Maximum a Posteriori Estimate —— MAP):视θ为一个随机变量,存在分布p(θ),将其先验分布带入,但仍然假设存在最优的参数. θ = argmax l(θ)*p(θ) (即假设θ也是随机变量,存在着先验分…

在Click Model中进行参数预估的方法有两种:最大似然(MLE)和期望最大(EM).至于每个click model使用哪种参数预估的方法取决于此model中的随机变量的特性.如果model中的随机变量都是可以observed,那么无疑使用MLE,而如果model中含有某些hidden variables,则应该使用EM算法. 1. THE MLE ALGORITHM 似然函数为: 则需要预估的参数的在似然函数最大时候的值为: 1)MLE FOR THE RCM AND CTR MODELS…

虽然openBugs效果不错,但原理是什么呢?需要感性认识,才能得其精髓. Recall [Bayes] prod: M-H: Independence Sampler firstly. 采样法 Recall [ML] How to implement a neural network then.     梯度下降法 And compare them. 梯度下降,其实就是减小loss function,不断逼近拟合的过程. 那采样法呢? y = a*x +sigma,  where sigma~…

以下是几种常见的离散型概率分布和连续型概率分布类型: 伯努利分布(Bernoulli Distribution):常称为0-1分布,即它的随机变量只取值0或者1. 伯努利试验是单次随机试验,只有"成功"(1)或"失败"(0)这两种结果.假如某次伯努利实验成功的概率为p,失败的概率为q=1-p,那么实验成功或失败的概率可以写成:. 伯努利分布的期望: 伯努利分布的方差: 二项分布(Binomial Distribution):用以描述n次独立的伯努利实验中有x次成功的…

Two Types of Estimation One of the major applications of statistics is estimating population parameters from sample statistics. There are types of estimation: Point Estimate: the value of sample statistics Point estimates of average height with multi…

Sampling and Estimation Sampling Error Sampling error is the difference between a sample statistic(the mean, variance, or standard deviation of the sample) and its corresponding population parameter(the true mean, variance, or standard deviation of t…

1.Normal distribution In probability theory, the normal (or Gaussian or Gauss or Laplace–Gauss) distribution is a very common continuous probability distribution. Normal distributions are important in statistics and are often used in the natural and…

A Statistical View of Deep Learning (II): Auto-encoders and Free Energy With the success of discriminative modelling using deep feedforward neural networks (or using an alternative statistical lens, recursive generalised linear models) in numerous in…

PRML Chapter 2. Probability Distributions P68 conjugate priors In Bayesian probability theory, if the posterior distributions p(θ|x) are in the same family as the prior probability distributionp(θ), the prior and posterior are then called conjugate d…