Estimation: Almost every is spent on ergod the text and build the dictionary. Gains: I have never used C# before. So This is a precious experience for me. I've learned the basic application of C#. I've tried my first time to write and the first time…

1.What is Maximum Likelihood? 极大似然是一种找到最可能解释一组观测数据的函数的方法. Maximum Likelihood is a way to find the most likely function to explain a set of observed data. 在基本统计学中,通常给你一个模型来计算概率.例如,你可能被要求找出X大于2的概率,给定如下泊松分布:X ~ Poisson (2.4).在这个例子中,已经给定了你泊松分布的参数 λ(2.4),…

Point Estimation \(\bullet\)What is point estimation? Example: \(\bullet\) Bevan, Kullberg, and Rice ( 1979) studied random fluctuations of current across a muscle cell membrane. The cell membrane contained a large number of channels, which opened an…

最近在菜鸟教程上自学redis.看到Redis HyperLogLog的时候,对"基数"以及其它一些没接触过(或者是忘了)的东西产生了好奇. 于是就去搜了"HyperLogLog",从而引出了Cardinality Estimation算法,以及学习它时参考的一些文章: http://blog.codinglabs.org/articles/algorithms-for-cardinality-estimation-part-i.html 从文章上看来,基数是指一个…

Notes from Notes on Noise Contrastive Estimation and Negative Sampling one sample: \[x_i \to [y_i^0,\cdots,y_{i}^{k}]\] where \(y_i^0\) are true labeled words , and \(y_i^1,\cdots,y_i^{k}\) are noise samples word index, which is generated by unigram…

Knowledge Discovery in Databases (KDD) is an active and important research area with the promise for a high payoff in many business and scientific applications. One of the main tasks in KDD is classification. A particular efficient method for classif…

http://blog.csdn.net/myarrow/article/details/51933651 1. 目前进展 1.1 相关资料      1)HANDS CVPR 2016      2)HANDS 2015 Dataset      3)CVPR 2016      4)Hand 3D Pose Estimation (Computer Vision for Augmented Reality Lab)          5)CVPR2016 Tutorial: 3D Deep…

对于SQL Server数据库来说,性能一直是一个绕不开的话题.而当我们去分析和研究性能问题时,执行计划又是一个我们一直关注的重点之一. 我们知道,在进行编译时,SQL Server会根据当前的数据库里的统计信息,在一定的时间内,结合本机资源,挑选一个当前最佳的执行计划去执行该语句. 那么数据库分析引擎如何使用这些统计信息的呢?数据库引擎会根据数据库里的统计信息,去计算每次操作大约返回多少行.这个动作称之为基数计算(cardinality estimation).数据库分析引擎会基于这些信息判断…

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…

High Performance My SQL, Third Edition Date and Time Types My SQL has many types for various kinds of date and time values, such as YEAR andDATE. The finest granularity of time My SQL can store is one second. (Maria DB hasmicrosecond-granularity temp…