6883e621_ws___Machine_Learning__Theory___3
[0] Graphical/generative Models [1] Sometimes Bayesian and sometimes not. Data-makers are typically assumed to be IID samples of fixed or varying length data. Data-makers are represented graphically with conditional independencies encoded in the graph. For some graphs, fast algorithms for making (or approximately making) predictions exist. [2] Relative to pure Bayesian systems, this approach is sometimes computationally tractable. More importantly, the graph language is natural, which aids prior elicitation. [3] 
Often (still) fails to fix problems with the Bayesian approach. In real world applications, true conditional independence is rare, and results degrade rapidly with systematic misspecification of conditional independence.
All Models of Learning have Flaws « Machine Learning (Theory)
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