In Chinese Restaurant 题目连接: http://acm.hust.edu.cn/vjudge/contest/123332#problem/B Description When Vova arrived in Guangzhou, his Chinese friends immediately invited him to a restaurant. Overall n people came to the restaurant, including Vova. The w…

Here is a note of Distance dependent Chinese Restaurant Processes 文章链接http://pan.baidu.com/s/1dEk7ZA5 1. Distance dependent CRPs In the traditional CRP ,the probability of a customer sitting at a table is computed from the number of other customers a…

题目链接:space=1&num=1725" target="_blank">http://acm.timus.ru/problem.aspx?space=1&num=1725 Every fall, all movies are shown to a full house at one of the most popular cinema theatres in Yekaterinburg because students like to spend…

也就是说假设空桌子有a0个人,然后顾客选择桌子的概率和桌子上人数成正比. 性质: 改变用户的排列方式,桌子的排列方式,概率不变换.…

题目链接:http://acm.timus.ru/problem.aspx?space=1&num=1984 1984. Dummy Guy Time limit: 0.5 second Memory limit: 64 MB Every year students of our university participate in Regional Contest in Saint Petersburg. Train journeys from Yekaterinburg to Saint Pe…

题目链接:http://acm.timus.ru/problem.aspx?space=1&num=1823 1823. Ideal Gas Time limit: 0.5 second Memory limit: 64 MB Many of you know the universal method of solving simple physics problems: you have to find in a textbook an identity in which you know t…

题目传送门(内部题25) 输入格式 一行三个整数$n,m,k$. 输出格式 一行一个整数表示答案. 样例 样例输入: 2 2 2 样例输出: 数据范围与提示 样例解释: $f_0=10,f_1=4,f_2=2,f_3=f_4=0$. 数据范围: 对于所有数据,$2\leqslant n,m\leqslant {10}^9,1\leqslant k\leqslant {10}^6$. 题解 考虑$\sum \limits_{i=0}^{nm}i\times f_i$的意义:所有方案中炼字的个数之和…

向量定义:x1 = c(1,2,3); x2 = c(1:100) 类型显示:mode(x1) 向量长度:length(x2) 向量元素显示:x1[c(1,2,3)] 多维向量:multi-dimensional vector:rbind(x1,x2); cbind(x1,x2) > x = c(1,2,3,4,5,6) > y = c(6,5,4,3,2,1) > z = rbind(x,y) > z [,1] [,2] [,3] [,4] [,5] [,6] x 1 2 3 4…

重要的是通过实践更深入地了解贝叶斯思想,先浅浅地了解下LDA. From: http://blog.csdn.net/huagong_adu/article/details/7937616/ 传统方法的缺陷: 传统判断两个文档相似性的方法是通过查看两个文档共同出现的单词的多少,如TF-IDF等,这种方法没有考虑到文字背后的语义关联,可能在两个文档共同出现的单词很少甚至没有,但两个文档是相似的. 在主题模型中,主题表示一个概念.一个方面,表现为一系列相关的单词,是这些单词的条件概率.形象来说,主题…

From: http://www.cnblogs.com/bayesianML/p/6377588.html#central_problem You can do it: Dirichlet Process, HDP, HDP-HMM, IBP, CRM, etc. 本文目录结构如下: 核心主题 中心问题 参数估计 模型比较 非贝叶斯方法 最大似然 正则化 EM算法 基本推断算法 MAP估计 Gibbs采样 马尔科夫链蒙特卡洛(MCMC) 变分推断(Variational inference)…