一.PGM用来做什么 1.  医学诊断:从各种病症分析病人得了什么病,该用什么手段治疗 2.  图像分割:从一张百万像素级的图片中分析每个像素点对应的是什么东西 两个共同点:(1)有非常多不同的输入变量:(2)对于算法而言,结果都是不确定的 二.PGM各代表什么 1.  Models 2.  Probabilistic (1)概率:设计model即是为了分析一些不确定的东西(uncertainty) (2)Uncertainty的来源: (3)概率在模型表达上的优势 3.  Graphical

课程链接:https://www.coursera.org/learn/probabilistic-graphical-models/lecture/KMjHs/reasoning-patterns 原CPD如下: 计算: p(d1|g3) = p(d1, g3) / p(g3) p(d1, g3) = p(g3, d1) = p(g3|d1, i0) * p(d1, i0) + p(g3|d1, i1) * p(d1, i1) = 0.7*0.4*0.7 + 0.2*0.4*0.3  约等于0

一.How to construct the dependency? 1.首字母即随机变量名称 2.I->G是更加复杂的模型,但Bayes里不考虑,因为Bayes只是无环图. 3.CPD = conditional probability distribution.图中的每一个点都是一个CPD,这里5个点,就有五个CPD. 二.Chain Rule for Bayesian Neatworks 将整个Bayes网络的所有节点所构成的联合概率(Joint probability)利用链式法则(ch

https://www.cs.cmu.edu/~epxing/Class/10708-17/slides/lecture1-Introduction.pdf Computational and CS orientated => DK and NF's book Statistical and easier one => Jordan's book MLAPP => also a good book HWs => Theory, algorithm design and implem

Not in the frontier of research, but the results are used commonly now. X_{k+1} - X_n are known, to calculate the joint probability, we have to do inference Recent research: on the approximate inference teches approx: 1) optimization-based 2) samplin

p_bar(x) = p(x,e) p(x) is p(x|e) p_bar is hard to estimate, but p(x) is easy and p(x) \propotion p_bar q is easier for sampling, while we cant sample from p Occam's Razor??? 原文地址:https://www.cnblogs.com/ecoflex/p/10259391.html

draw a topic h and then draw independent xfrom multinomial distribution A paper can have several topics, and each topics has different proportions O is the matrix of topic word, given by the model h is the topic 原文地址:https://www.cnblogs.com/ecoflex/p

此文为David M. Blei所写的<Introduction to Probabilistic Topic Models>的译文,供大家参考. 摘要:概率主题模型是一系列旨在发现隐藏在大规模文档中的主题结构的算法.本文首先回顾了这一领域的主要思想,接着调研了当前的研究水平,最后展望某些有所希望的方向.从最简单的主题模型--潜在狄立克雷分配(Latent Dirichlet Allocation,LDA)出发,讨论了其与概率建模的联系,描述了用于主题发现的两种算法.主题模型日新月异,被扩展和

This article introduces the networking part of Elasticsearch. We look at the network topology of an Elasticsearch cluster, which connections are established between which nodes and how the different Java clients works. Finally, we look a bit closer o