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 empty network?
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: Modeling Relationship Strength in Online Social Networks. They suggest to use following architecture Here S(ij) represents vector of similarity between user i and j - Observed z(ij) is a hidden variable - relationship strength (Normal distribution regularised …
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 answer could be similar to the following, but for bayes Networks: Hidden Markov Models A Hidden Markov Model (HMM) is a 5-tuple $\lambda = (S, O, A, B, \Pi)$: $S \neq \emptyset$: A set of states (e.g. "beginning …
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: Kaggle Rain prediction, also the 2nd place Grasp and Lift 2nd also third place - Identify hand motions from EEG recordings I'm not too sure about ASR (automatic speech recognition) and machine translation, but I think I've also heard that (recurrent) neural networks (start …
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 Markov random field is a conditional random field, in which each random variable may also be conditioned upon a set of global observations o. This would mean that CRFs are a special case of MRFs. …