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Timeline for Detecting Bimodal Distribution

Current License: CC BY-SA 3.0

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Dec 30, 2014 at 9:47 vote accept concept3d
Dec 14, 2014 at 8:13 answer added Aleksandr Blekh timeline score: 7
Dec 14, 2014 at 6:58 history edited concept3d CC BY-SA 3.0
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Dec 14, 2014 at 6:52 history edited concept3d CC BY-SA 3.0
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Dec 11, 2014 at 22:28 comment added Glen_b Ah, a two-component mixture of normals (and if bimodality is actually required to hold, then it's a bimodal two-component mixture of normals).
Dec 11, 2014 at 18:50 comment added concept3d @Glen_b as I stated i am new to this and might used the wrong term. What I meant is a population with two gaussian distributions if that doesn't make sense please correct me
Dec 11, 2014 at 16:06 comment added Glen_b What is a 'bimodal normal distribution'?
Dec 11, 2014 at 15:37 comment added Nick Cox If the problem is working from a histogram, the origin of data as a time series may be irrelevant.
Dec 11, 2014 at 15:36 comment added Nick Cox There is no objective detection of bimodality without some criterion for the "strength" of a mode. You need to worry not only about unimodality vs bimodality but bimodality vs multimodality. See e.g. work on Minotte and co-workers on "mode trees" (citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.30.5736)
Dec 11, 2014 at 15:21 comment added concept3d @gung those are histograms, X is energy, Y is frequency/probability.
Dec 11, 2014 at 15:18 comment added Stephan Kolassa You may want to read up on mixture models.
Dec 11, 2014 at 15:12 answer added IrishStat timeline score: 2
Dec 11, 2014 at 14:45 comment added gung - Reinstate Monica What is the nature of the data? Are these single values (eg intensities) over time, or are they histograms?
Dec 11, 2014 at 14:25 review First posts
Dec 11, 2014 at 14:45
Dec 11, 2014 at 14:21 history asked concept3d CC BY-SA 3.0