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Opportunity for Comment!

(This is Dan)

Last September, Jonah, Aki, Michael, Andrew and I wrote a paper on the role of visualization in the Bayesian workflow.  This paper is going to be published as a discussion paper in the Journal of the Royal Statistical Society Series A and the associated read paper meeting (where we present the paper and people present discussions) will take place at the RSS conference in Cardiff on 5 September, 2018.

The instructions for submitting a discussion on the paper (either in person or via email) are given here. The gist is in this paragraph:

Further shorter comments (up to five minutes or 400 words in writing) are then invited from the audience. If the two hours in total permit before the authors are given the opportunity to respond very briefly verbally (and at greater length in writing in the journal), the secretary for the meeting will read out any contributions that have been sent in from people who cannot be present. Contributions in writing (again, 400 words maximum) are welcome up to two weeks after the meeting.

If anyone has thoughts on that paper or what we could’ve done better or differently, now is y0ur opportunity to say so in print!

There will be two other papers presented at the same meeting (Graphics for uncertainty by Adrian Bowman and Visualizing spatiotemporal models with virtual reality: from fully immersive environments to applications in stereoscopic view by Stefano Castruccio, Marc Genton, and Ying Sun), so it’s sure to be a barn-burner. You should read them and submit comments on them too if you’re so inclined!

3 Comments

  1. I wrote a blog on visualizing uncertainty some time ago.
    https://ctg2pi.wordpress.com/2015/02/24/principles-of-posterior-visualization/
    Good someone else is writing proper papers on this topic.

    • Aki Vehtari says:

      Thanks for the link to your interesting post and nice visualizations!

    • Keith O'Rourke says:

      Nice clear and concise post.

      In the them of “Opportunity for Comment!” some would argue (e.g. Dan Simpson) that you really can’t separate the prior and the data generating model (likelihood) but need to keep the full joint model intact. This would suggest the posterior is not _sufficient_ for Bayesian visualization.

      I have argued that both the prior and posterior should always be visualized if not also the likelihood (posterior/prior). I also went further and suggested the individual unit of analysis likelihoods that multiply up to the likelihood should also be visualized http://andrewgelman.com/wp-content/uploads/2011/05/plot13.pdf

      The paper tends to receive two responses, nice ideal but almost trivial and makes absolutely no sense at all.

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