斯坦福-随机图模型-week3.1_

简介: title: 斯坦福-随机图模型-week3.1tags: notenotebook: 6- 英文课程-9-Probabilistic Graphical Models 1: Representation---斯坦福-随机图模型-week3.

title: 斯坦福-随机图模型-week3.1
tags: note
notebook: 6- 英文课程-9-Probabilistic Graphical Models 1: Representation
---

斯坦福-随机图模型-week3.1

习题题

1. Question 1

Factor Scope. Let ϕ(a,b) be a factor in a graphical model, where a is a value of A and b is a value of B. What is the scope of ϕ?

{A}

{A, B, C, D, E}

{A, B, C}

{A, B}

Correct 

Question 2

Independence in Markov Networks. Consider this graphical model from week 1's quizzes. This time, all of the edges are undirected (see modified graph below). Which pairs of variables are independent in this network? You may select 1 or more options.

img_a02dae06126b6c6fa28b7c197930111f.jpe

No pair of variables are independent on each other.

Correct 
No pairs of variables are independent in a fully connected Markov network.

B, E

Un-selected is correct 

C, D

Un-selected is correct 

Question 3

Factorization. Which of the following is a valid Gibbs distribution over this graph?

img_8b481b85b685b99444e256cfd740d3fb.jpe

ϕ(A,B,C,D,E,F)

ϕ(A)×ϕ(B)×ϕ(C)×ϕ(D)×ϕ(E)×ϕ(F)Z, where Z is the partition function

Correct 
A Gibbs distribution is a factor product divided by the partition function, and this expression complies with this definition.

ϕ(A)×ϕ(B)×ϕ(C)×ϕ(D)×ϕ(E)×ϕ(F)

ϕ(A,B,D)×ϕ(C,E,F)Z, where Z is the partition function

Question 4

Factors in Markov Network. Let ϕ(A,B,C) be a factor in a probability distribution that factorizes over a Markov network. Which of the following must be true? You may select 1 or more options.

ϕ(a,b,c)≥0, where a is a value of A, b is a value of B, and c is a value of C.

Correct 

ϕ(a,b,c)≤1, where a is a value of A, b is a value of B, and c is a value of C.

Un-selected is correct 

A, B, and C do not form a clique in the network.

Un-selected is correct 

There is a path connecting A, B, and C in the network.

Correct 

A, B, and C form a clique in the network.

Correct 
相关文章
[8585014]斯坦福-随机图模型-week3.2_
title: 斯坦福-随机图模型-week3.2 tags: note notebook: 6- 英文课程-9-Probabilistic Graphical Models 1: Representation --- 斯坦福-随机图模型-week3.2 独立马尔科夫网络 分离的概念 分离的概念是这样的,如果我们有这样的定义: 如果再H中没有没有实际的连接线,我们认定Z呗H分离 比如再这个图中,图A和E是被B和C分离的。
839 0
斯坦福-随机图模型-week3.2_
title: 斯坦福-随机图模型-week3.2 tags: note notebook: 6- 英文课程-9-Probabilistic Graphical Models 1: Representation --- 斯坦福-随机图模型-week3.2 独立马尔科夫网络 分离的概念 分离的概念是这样的,如果我们有这样的定义: 如果再H中没有没有实际的连接线,我们认定Z呗H分离 比如再这个图中,图A和E是被B和C分离的。
826 0
|
决策智能
斯坦福-随机图模型-week4.0_
title: 斯坦福-随机图模型-week4.0 tags: note notebook: 6- 英文课程-9-Probabilistic Graphical Models 1: Representation --- 斯坦福-随机图模型-week4.0 最大期望收入模型 简答的决策 我们使用随机图模型进行决策需要的原料是什么ne ? 我们需要决策的情景 一些列的可能的行为 一系列的转台量: 还有一个收益函数 期望的收益公式: 期望收益公式表示是这样的,每个行为的可能性,乘以他的期望收益的加权和。
773 0
斯坦福-随机图模型-week4.2_
title: 斯坦福-随机图模型-week4.2 tags: note notebook: 6- 英文课程-9-Probabilistic Graphical Models 1: Representation --- 斯坦福-随机图模型-week4.
728 0
|
定位技术
斯坦福-随机图模型-week3.3_
title: 斯坦福-随机图模型-week3.3 tags: note notebook: 6- 英文课程-9-Probabilistic Graphical Models 1: Representation --- 斯坦福-随机图模型-week3.
670 0
|
定位技术
[8584966]斯坦福-随机图模型-week3.3_
title: 斯坦福-随机图模型-week3.3 tags: note notebook: 6- 英文课程-9-Probabilistic Graphical Models 1: Representation --- 斯坦福-随机图模型-week3.
864 0
斯坦福-随机图模型-week3.0_
title: 斯坦福-随机图模型-week3.0 tags: note notebook: 6- 英文课程-9-Probabilistic Graphical Models 1: Representation --- 斯坦福-随机图模型-week3.0 马尔科夫网络 pairwise markov networks 成对马尔科夫模型 图论模型中有有向图和无向图,对于无向图来说,运用到随机图论中就是马尔科夫模型。
765 0
斯坦福-随机图模型-week2.4_
title: 斯坦福-随机图模型-week2.4 tags: note notebook: 6- 英文课程-9-Probabilistic Graphical Models 1: Representation --- 斯坦福-随机图模型-week2.
1069 0
|
人工智能 BI
斯坦福-随机图模型-week2.2_
title: 斯坦福-随机图模型-week2.2 tags: note notebook: 6- 英文课程-9-Probabilistic Graphical Models 1: Representation --- 斯坦福-随机图模型-week2.2 习题 1。
1029 0
斯坦福-随机图模型-week2.3_
title: 斯坦福-随机图模型-week2.3 tags: note notebook: 6- 英文课程-9-Probabilistic Graphical Models 1: Representation --- 斯坦福-随机图模型-week2.3 局部结构-总论 tabular representation 表格表示 CPD 条件概率分布 我们以前使用表格来描述各个情况的概率,比如这样: 比如我们讨论学生问题,这个表可以描述智商,难度和得分的关系。
823 0