Stack Exchange Network

Stack Exchange network consists of 175 Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their knowledge, and build their careers.

Visit Stack Exchange

Questions tagged [graphical-models]

The tag has no usage guidance.

2
votes
1answer
20 views

How do you marginalize in graphical model elimination?

I'm reading Michael I. Jordan's book on probabilistic graphical models, and I don't understand the elimination algorithm presented in chapter 3. To narrow the question down, consider page 6. In ...
0
votes
0answers
11 views

Learning evolutionary structure of a network

I know that structure learning in a probabilistic graphical model can be hard in general and needs exponential data. Are there some results that if have some assumptions about the underlying structure,...
0
votes
2answers
48 views

What is the difference between “use case” and “function”?

In the use case diagram, we draw use cases as an ellipse. Are the use cases the same as functions? I mean by functions, those functions which we write in the program.Thank you in advance.
1
vote
1answer
26 views

Transforming undirected maximum spanning tree into directed augmented network

I am having trouble transforming a maximum weighted spanning tree into a directed tree such that each node is allowed at most one parent node. Taken from page 141 Friedman et. al (1997), the outline ...
1
vote
0answers
37 views

DAGs and Equivalence Class of DAGs

I am learning DAGs and Equivalence Class of DAGs, I am reading the material by Prof. Campos Ibáñez here: https://www.cs.cmu.edu/afs/cs/project/jair/pub/volume18/acid03a-html/node2.html However, I ...
0
votes
1answer
85 views

How static Bayesian networks are stored? Like Hugin etc

I wanted to know the standard format to store the Bayesian network structure? I came across Hugin format and others. But couldn't find out the documentation as how its been written, if I wanted to ...
0
votes
1answer
218 views

Comparative study between Deep neural nets and Bayesian Networks

Is there any comparative study that showcases the powers of Bayesian Networks and Deep learning in their respective favorable setup and how they compare? I tried to go through blogs but couldn't find ...
1
vote
0answers
107 views

Variable elimination in Bayesian network

I'm trying to check if my understanding of variable elimination is correct. Assume the above Bayesian network is factorized as: $p(a,b,d,e,l,s,t,x) = p(a)p(t|a)p(e|t,l)p(x|e)p(l|s)p(b|s)p(d|b,e)p(s)$...
1
vote
0answers
24 views

About the complexity of learning probabilistic graphical models

I guess that one way of measuring the complexity of learning a joint probability distribution is as its "sample complexity" (which is also sometimes known as its "distributional learning complexity"?) ...
3
votes
0answers
53 views

Have people looked at “Hypergraphical” models?

Graphical models are a very useful tool with many applications, whereby a joint distribution of a set of random variables is modeled using only pairwise dependencies between the variables, and two ...
3
votes
1answer
995 views

Difference between Bayesian Networks and Dynamic Bayesian Networks

I'm studying Bayesian networks and want to clarify a couple of things with people who are more knowledgable in the area than me. As far as I understand it, a Bayesian network (BN) is a directed ...
1
vote
0answers
570 views

Maximum likelihood estimate for softmax function

Given an undirected graphical model with no edges and only N nodes, I am trying to find a closed form solution to the ML estimate of each node given that $p(x|\theta)=\frac{\exp(\sum_{s\in V}\...
2
votes
1answer
678 views

Bayes net: algorithm to calculate joint distribution?

I recently started studying bayesian networks and I am now implementing an exact inference algorithm: enumeration. I am aware of the complexity and inefficiency of this method but I want to fully ...