https://www.quora.com/What-are-some-applications-of-Probabilistic-Graphical-Models https://www.quora.com/What-are-some-applications-of-Probabil... I'd say for

https://www.quora.com/What-are-some-applications-of-Probabilistic-Graphical-Models https://www.quora.com/What-are-some-applications-of-Probabil... I'd say for people with interest in ML and DL, the main insight from PGMs is intuitions about probability theory, especially conditional probabilities and the concept of (un)conditional independence. State of the art in which PGMs would be relevant is currently mostly brute-force learning via stochastic gradient descent (with momentum) in deep directed models/function approximators, but function approximation likely does not solve all problems as adversarial examples show. There are certainly very important applications of PGMs in all kinds of domains as listed in the link above (perhaps I would have added FastSLAM [1]), and there are also variational autoencoders/Helmholz machines and (Deep) Boltzmann Machines, which are especially interesting for research perspective. [1] http://robots.stanford.edu/papers/montemerlo.fastslam-tr.pdf http://robots.stanford.edu/papers/montemerlo.fastslam-tr.pdf