What do you do to visualize uncertainty?

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Howard Wainer writes:

What do you do to visualize uncertainty?
Do you only use static methods (e.g. error bounds)?
Or do you also make use of dynamic means (e.g. have the display vary over time proportional to the error, so you don’t know exactly where the top of the bar is, since it moves while you’re watching)?

Have you any thoughts on this topic?
I assume that since a Bayesian generates a posterior dist’n the output should not be point but rather a dist’n; and you being the most prolific Bayesian I know that you’ve got three or four old papers that you’ve written on it.

OK, sure, when you put it that way, my collaborators and I do have a few papers on the topic:

Visualization in Bayesian data analysis

Visualizing distributions of covariance matrices

Multiple imputation for model checking: completed-data plots with missing and latent data

A Bayesian formulation of exploratory data analysis and goodness-of-fit testing

All maps of parameter estimates are misleading

But I don’t really have much else to say right now. Dynamic graphics seem like a good idea but I’ve never programmed them myself. In many settings it will work to display point estimates, but sometimes this can create big problems (as discussed in some of the above-linked papers) because Bayesian point estimates will tend to be too smooth—less variable—compared to the variation in the underlying parameters being modeled.

So I’m kicking this one out to the commenters to see if they can offer some useful suggestions.

10 thoughts on “What do you do to visualize uncertainty?”

  7. This line “In the Bayesian sense, looking at inferences and deciding whether they “make sense” can be interpreted to be a comparison of the estimates to our prior knowledge, that is, to a prior model” suggests to me that posterior predictive estimates need to be compared to prior predictive estimates?
    (OK, if prior predictive estimates are _known_ to be just vague noise, perhaps that need not be explicitly plotted.)

    Also, not sure plotting in the data space is always most informative, though I must admit I was not able to be very convincing about the value of plotting in the parameter space http://statmodeling.stat.columbia.edu/2011/05/14/missed_friday_t/

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