Distance dependent Chinese Restaurant Processes
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 already sitting at that table.
Now we introduce the distance dependent CRP, the seating plan probability is described in terms of the probability of a customer sitting with each of the other customers .
let denote the i th customer assignment ,the index of the customer with whom the i th customer is sitting ,let denote the distance measurement between customers i and j , let D denote the set of all distance measurements between all customers ,and let be a decay function .
Notice that the customer assignments do not depend on other customer assignment , only the distances between customers.
This distribution is determined by the nature of the distance measurements and the decay function .For many sets of distance measurements ,the resulting distribution over partition is no longer exchangeable ;this is an appropriate distribution to use when exchangeability is not a reasonable assumption.
2.The decay function:
In general the decay function mediates how distances between customers affect the resulting distribution over partitions .Function f is non-increasing , takes non-negative finite values ,and satisfies f(∞)=0。 (衰减函数的性质)
3. Sequential CRPs and the traditional CRP
A sequential CRP is constructed by assuming that dij=∞ for those j>i ,and this guarantees that no customer can be assigned to a later customer.And when f(d)=1 for d≠∞ and dij<∞ for j<i, the sequential CRP is can re-express the traditional CRP.
NOTICE : although these models are the same ,the corresponding Gibbs samplers are different .(why ?)
4. Marginal invariance:
The traditional CRP is marginally invariant : Marginalizing over a particular customer gives the same probability distribution as if that customer were not included in the model at all .But the DDCRP does not have this property ,and this paper gives us two example of the relevant property of DDCRPS.
Language modeling : a fully observed model
Mixture modeling: a mixture model
5. Relationship to dependent Dirichlet processes (DDP):(they are both infinite clustering model that models dependencies between the latent component assignments of the data )
The first difference is that the dependent Dirichlet process mixture use the truncations of the stick-breaking representation for approximate posterior inference ,in CONTRAST, the ddCRP mixtures are amenable to Gibbs sampling algorithms . Another difference is that the spirit behind them ,in the DDP, data are drawn from distributions that are similar to distributions of nearby data,and the particular values of the nearby data impose softer constraints than those in the ddCRP.(区分ddCRP与贝叶斯非参数模型)
Distance dependent Chinese Restaurant Processes的更多相关文章
- URAL 1962 In Chinese Restaurant 数学
In Chinese Restaurant 题目连接: http://acm.hust.edu.cn/vjudge/contest/123332#problem/B Description When ...
- Distance Dependent Infinite Latent Feature Model 阅读笔记1
阅读文献:Distance Dependent Infinite Latent Feature Model 作者:Samuel J.Gershman ,Peter I.Frazier ,and Dav ...
- 中国餐馆过程(Chinese restaurant process)
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.
- Marginalize
在David M.Blei 的Distance Dependent Chinese Restaurant Processes 中提到:DDCRP 的一个重要性质,也是和dependent DP 的一个 ...
- 100 Most Popular Machine Learning Video Talks
100 Most Popular Machine Learning Video Talks 26971 views, 1:00:45, Gaussian Process Basics, David ...
- ICLR 2013 International Conference on Learning Representations深度学习论文papers
ICLR 2013 International Conference on Learning Representations May 02 - 04, 2013, Scottsdale, Arizon ...
- 关于LDA的文章
转:http://www.zhizhihu.com/html/y2011/3228.html l Theory n Introduction u Unsupervised learning by ...
- Bayesian machine learning
from: http://www.metacademy.org/roadmaps/rgrosse/bayesian_machine_learning Created by: Roger Grosse( ...
- R Language
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-dimen ...
随机推荐
- SpringBoot简介与快速入门
一.SpringBoot简介 1.1 原有Spring优缺点分析 1.1.1 Spring的优点分析 Spring是Java企业版(Java Enterprise Edition,JEE,也称J2EE ...
- $ZOJ\ 2432\ Greatest\ Common\ Increasing\ Subsequence$
传送门 $Description$ 求两个序列的最长公共上升子序列 $Solution$ $f[i][j]$表示$a$序列匹配到$i$和$b$序列匹配到$j$的最长上升序列的长度,这里并不要求$a[i ...
- Nginx 究竟如何处理事件?
在了解了网络事件以及事件分发收集器以后,让我们来了解 Nginx 是怎么样处理事件的? Nginx 事件循环 当 Nginx 刚刚启动时,在等待事件部分,也就是打开了 80 或 443 端口,这个时候 ...
- python版飞机大战代码简易版
# -*- coding:utf-8 -*- import pygame import sys from pygame.locals import * from pygame.font import ...
- 1059 C语言竞赛 (20 分)C语言
C 语言竞赛是浙江大学计算机学院主持的一个欢乐的竞赛.既然竞赛主旨是为了好玩,颁奖规则也就制定得很滑稽: 0.冠军将赢得一份"神秘大奖"(比如很巨大的一本学生研究论文集--). 1 ...
- 单机Web后端接口服务压力测试
单机Web后端接口服务压力测试 工具:Apache jmeter 环境:Window 10 语言:Kotlin + java 架构:SpringBoot + + Mysql + redis + Spr ...
- 查找2-n之间素数的个数
题目描述 查找2-n之间素数的个数.n为用户输入值.素数:一个大于1的正整数,如果除了1和它本身以外,不能被其他正整数整除,就叫素数.如2,3,5,7,11,13,17…. 输入 整数n 输出 2-n ...
- OpenStack Identity API v3
Table Of Contents OpenStack Identity API v3 What’s New in Version 3.7 What’s New in Version 3.6 What ...
- Python - 线性回归(Linear Regression) 的 Python 实现
背景 学习 Linear Regression in Python – Real Python,前面几篇文章分别讲了"regression怎么理解","线性回归怎么理解& ...
- 三、Spring Cloud之软负载均衡 Ribbon
前言 上一节我们已经学习了Eureka 注册中心,其实我们也使用到了Ribbon ,只是当时我们没有细讲,所以我们现在一起来学习一下Ribbon. 什么是Ribbon 之前接触到的负载均衡都是硬负载均 ...