Data Science Stack Exchange
Data Science Stack Exchange

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.

Examples for Probabilistic Graphical Models (PGMs) are:

  • Markov Random Fields (MRFs, undirected)
    • Conditional Random Fields (CRFs)
  • Bayesian networks (BNs, directed)
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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: Kaggle Molecular Activity Challenge Regression:…
Martin Thoma
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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 somebody please explain? It would be nice if your…
Martin Thoma
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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 random field it says: One notable variant of a…
Martin Thoma
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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. Does it mean that the best network is always the…
jamesmiraflor
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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 for Social Network Analysing from this paper:…
Liz
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