Directed graphical models

September 20, 2017 — May 13, 2020

graphical models
hidden variables
hierarchical models
machine learning
Figure 1

Graphs of conditional, directed independence are a convenient formalism for many statistical models. If you have some kind of generating process for a model, often the most natural type of graphical model to express it with is a DAG. These are also called Bayes nets (not to be confused with Bayesian inference.)

These can even be causal graphical models, and when we can infer those we are extracting Science (ONO) from observational data. See causal graphical models.

The laws of message passing inference assume their (MO) most complicated form for directed models; in practice it is frequently easier to convert a directed model to a factor graph for implementation. YMMV.

Figure 2: I found James F Fixx’s puzzle book on the shelf when writing this post

1 Simpson’s paradox

Simpson’s paradox is an evergreen example of the importance of that causal graph. For a beautiful and clear example see Allen Downey’s Simpson’s Paradox and Age Effects. It is also the key explanation in Michael Nielson’s Reinventing Explanation.

2 Tools

BayesNets is a Julia package for reasoning over directed graphical models.

Figure 3: Inferring the optimal intervention requires accounting for which arrows are independent of which.

3 References

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