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# NIPS Experiment Analysis

Sorry for the relative silence on the NIPS experiment. Corinna and I have both done some analysis on the data. Over the Christmas break I focussed an analysis on the ‘raw numbers’ which people have been discussing. In particular I wanted to qualify the certainties that people are placing on these numbers. There are a couple of different ways of doing this, bootstrap, or a Bayesian analysis. I went for the latter. Corinna has also been doing a lot of work on how the scores correlate, and the ball is in my court to pick up on that. However, before doing that I wanted to make the initial Bayesian analysis of the data. In doing so, we’re also releasing a little bit more information on the numbers.

Headline figure is that if we re-ran the conference we would expect anywhere between **38% and 64% of the same papers** to have been presented again. This is the figure that several commentators mentioned that is the one attendees are really interested in. Of course, when you think about it, you also realise it is a difficult figure to estimate because you reduce the power of the study because the figure is based only on papers which had at least one accept or more (rather than the full 168 papers used in the study).

Anyway details of the Bayesian analysis are available in a Jupyter notebook on github.