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Questions tagged [graphical-models]

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Generalization of a Markov random field and a Bayesian network?

I am seeking a graphical model that is a generalization of both a Markov random field (MRF) and a Bayesian network (BN). From the Markov random field wiki page: A Markov network or MRF is similar ...
jonem's user avatar
  • 371
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0 answers
39 views

Is there any toolbox for Markov Random Field Structure Learning?

I need a toolbox or software that takes a dataset as input, detect independencies among its random variables and produces the relative Markov Random Field graphical structure from that. Can anyone ...
Masih Zaamari's user avatar
1 vote
0 answers
101 views

Calculate probability in graphical model

I have the following graphical model, in which I wish to compute $p(Intelligence = 1|Letter = 1, SAT = 1)$ But I'm not sure how to rewrite $p(Intelligence = 1|Letter = 1, SAT = 1)$? I was told to ...
LRS25's user avatar
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1 answer
27 views

Finding most likely tree over a semilattice

If I am not mistaken, then a semilattice defines a finite set of trees, for example spanning trees. Now assume that each semilattice edge is annotated with a transition probability. In addition, let'...
Radio Controlled's user avatar
2 votes
1 answer
29 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 ...
Philip Raeisghasem's user avatar
0 votes
3 answers
905 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.
Mohamed Ashraf's user avatar
1 vote
1 answer
99 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 ...
J. Mathews's user avatar
1 vote
0 answers
56 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 ...
user2842390's user avatar
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1 answer
111 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 ...
letsBeePolite's user avatar
0 votes
1 answer
241 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 ...
letsBeePolite's user avatar
1 vote
0 answers
380 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: $p(a,b,d,e,l,s,t,x) = p(a)p(t|a)p(e|t,l)p(x|e)p(l|s)p(b|s)p(d|b,e)p(s)$...
beginner's user avatar
1 vote
0 answers
28 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"?) ...
gradstudent's user avatar
3 votes
0 answers
58 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 ...
Zur Luria's user avatar
  • 349
3 votes
1 answer
2k 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 ...
jonem's user avatar
  • 371
2 votes
0 answers
699 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}\...
Aden Dong's user avatar
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2 votes
1 answer
997 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 ...
jrlainfiesta's user avatar