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How are Bayesian networks related to deep belief networks? Are they the same?

From the post What is the difference between a neural network and a deep belief network?,
I gathered that deep belief networks are like deep neural networks where the weights and biases are set by iterations of an unsupervised algorithm. (I hope I'm understanding that right anyway.)

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No, Bayesian network and deep belief network are not the same thing.

  • Bayesian network is a type of probabilistic graphical model where vertexes are random variables and edges are conditional dependencies. For large number of random variables, we use the graphical structure assumptions to decompose the joint distribution in a manageable level. In Bayesian network, there are two major tasks, learning and inference. The ultimate goal of learning is getting the joint distribution of the data, and the goal of inferences is trying to calculate the probability for a given events, assuming you already have the model

  • As mentioned in the link you provided:

"Neural networks" is a term usually used to refer to feedforward neural networks. Deep Neural Networks are feedforward Neural Networks with many layers.

You can think about neural network is just a big non-linear function, that it can approximate complicated outcomes. Here the nodes are "neurons" and the edges are "connections", where they are essentially building blocks for a function.

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  • $\begingroup$ "The ultimate goal of learning is getting the joint distribution of the data", why do we specifically care about the joint distribution of the data/variables? $\endgroup$ – nbro May 11 at 12:59
  • $\begingroup$ So, what is the difference between Bayesian networks and deep belief networks? You forgot to explain it. $\endgroup$ – nbro May 11 at 13:01

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