Questions tagged [pgm]

A probabilistic graphical model (PGM) is a probabilisic model for which conditional dependencies are expressed with a graph G = (X, E) where X are random variables.

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Probabilistic Graphical Models - Topological Ordering of the Vertices

I have this graph, the vertices of which are to be assigned a topological ordering. That is, referring to the Figure attached: node 6 has topological order 1, since no edge ends up in 6. 7 has ...
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How to do hidden variable learning in Bayesian Network with Python?

I learned how to use libpgm in general for Bayesian inference and learning, but I do not understand if I can use it for learning with hidden variable. More precisely, I am trying to implement approach ...
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Is the maximum BDeu Bayesian Network always the empty network?

I'm recently reading a paper about Scoring Mechanisms for Bayesian Networks. For the BDeu score, it appears that the maximum possible score of BDeu for Bayesian Network structure learning is zero. ...
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What is the difference between a (dynamic) Bayes network and a HMM?

I have read that HMMs, Particle Filters and Kalman filters are special cases of dynamic Bayes networks. However, I only know HMMs and I don't see the difference to dynamic Bayes networks. Could ...
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Is there any domain where Bayesian Networks outperform neural networks?

Neural networks get top results in Computer Vision tasks (see MNIST, ILSVRC, Kaggle Galaxy Challenge). They seem to outperform every other approach in Computer Vision. But there are also other tasks: ...
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What is the relationship between Markov Random Fields and Conditional Random Fields?

In Neural networks [3.8] : Conditional random fields - Markov network by Hugo Larochelle it seems to me that a Markov Random Field is a special case of a CRF. However, in the Wikipedia article Markov ...