

A Brief Introduction to Graphical Models and Bayesian Networks (1998) - dstein64
http://www.cs.ubc.ca/~murphyk/Bayes/bnintro.html

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bra-ket
it would be interesting to know how graphical models can be integrated with
deep learning, and specifically how causal relationships can be inferred with
neural networks

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tabacof
Causality is a bit harder to integrate with current machine learning models as
it's hard even with standard probabilistic graphical models. On the other
hand, there has been a lot of work integrating deep neural networks with
probabilistic models.

For example, the variational auto-encoders are a graphical model with Gaussian
latent variables whose mean and variance are determined by (deep) neural
networks [1]. There has been work exploring the neural network weights as
latent variables themselves [2]. Finally, some new developments such as
dropout can be interpreted as some form of deep Gaussian processes [3].

I believe there will be a lot further developments on this area in the near-
future.

[1] [http://arxiv.org/abs/1312.6114](http://arxiv.org/abs/1312.6114)

[2] [http://arxiv.org/abs/1505.05424](http://arxiv.org/abs/1505.05424)

[3] [http://arxiv.org/abs/1506.02142](http://arxiv.org/abs/1506.02142)

