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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 out any experimental study where the respective models where described based on a example?

Rationale in putting this question: I am planning to work on Bayesian networks and I know they are great for inference and answering complex queries. But I couldn't find any convincing experimental results where they seemed to be more favorable as compared to other sophisticated machine learning tools like deep neural nets or conventional deep neural nets.

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  • $\begingroup$ What do you mean by comparative study? Do you just mean, one example where deep learning is used, and one where Bayesian networks are used? Or do you mean something different? Can you edit the question to clarify what you're looking for? $\endgroup$ – D.W. Jul 7 '16 at 5:40
  • $\begingroup$ As new and industry-driven as deep learning is (?), are there many (rigorous) papers about it at all? $\endgroup$ – Raphael Jul 7 '16 at 6:45
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They're not directly comparable. They do different things. They solve different problems.

A Bayesian network is a probabilistic model of the relationship between multiple random variables. It is a generative model. It builds a model of the joint probability distribution between multiple random variables. It typically requires some priors or assumptions about the structure of the joint distribution. While it could be used for classification, it normally isn't.

Deep learning is typically used for classification: supervised learning. It is a discriminative model. It does not try to model/estimate the joint probability distribution between multiple random variables. It typically does not require you to specify priors or assumptions about the structure of the joint distribution.

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