“Principles of posterior visualization”

What better way to start the new year than with a discussion of statistical graphics.

Mikhail Shubin has this great post from a few years ago on Bayesian visualization. He lists the following principles:

Principle 1: Uncertainty should be visualized

Principle 2: Visualization of variability ≠ Visualization of uncertainty

Principle 3: Equal probability = Equal ink

Principle 4: Do not overemphasize the point estimate

Principle 5: Certain estimates should be emphasized over uncertain

And this caution:

These principles (as any visualization principles) are contextual, and should be used (or not used) with the goals of this visualization in mind.

And this is not just empty talk. Shubin demonstrates all these points with clear graphs.

Interesting how this complements our methods for visualization in Bayesian workflow.

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