Here, we issue a challenge of optimizing a histogram given a set of data points or a series of event times.
We believe that you can write a paper, if you succeed in constructing an
algorithm that consistently beats ours. |
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Congratulations! (Jan. 16, 2016)
An algorithm presented by Max Murphy (University of Kansas) beat the Omi-Shinomoto
method 14 times out of 20 ! |
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| (1) Download three sets of event times, which are drawn from hidden underlying
rate processes chosen randomly. Event times are not necessarily derived
from the Poisson process. The possible methods of drawing event times are
described in Ref.[1]. |
| (2) Guess optimal bin sizes for three sets of data so that the histograms
best express unknown underlying rate processes. |
(3) See your histograms and those determined by the rules of Omi &
Shinomoto (2011), Shimazaki & Shinomoto (2007), Scott (1979), and Sturges
(1926), by comparison with true underlying rates. These five kinds of histograms
will be evaluated in terms of L2 and L1 errors between the histograms and the underlying rate. Your overall ranking will
be given according to the average of those rankings.
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