Timeline for Generating fake raw data from a given distribution
Current License: CC BY-SA 3.0
14 events
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| Sep 2, 2017 at 13:09 | history | tweeted | twitter.com/StackMma/status/903968043415588864 | ||
| Aug 23, 2017 at 3:31 | comment | added | JimB | Given the example you've added, you should consider taking the logs of the bin boundaries and see mathematica.stackexchange.com/questions/35588/… for a visual display. | |
| Aug 22, 2017 at 21:28 | comment | added | JimB | Because all of the data is binned, it's completely censored and not just because the rightmost interval is open. | |
| Aug 22, 2017 at 21:19 | history | edited | astrsk | CC BY-SA 3.0 |
supplied data
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| Aug 22, 2017 at 17:26 | answer | added | JimB | timeline score: 4 | |
| Aug 22, 2017 at 11:18 | answer | added | Szabolcs | timeline score: 4 | |
| Aug 22, 2017 at 10:20 | comment | added | Szabolcs |
Alternatively, you can apply HistogramDistribution to a WeightedData object to do this in a fully documented way.
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| Aug 22, 2017 at 10:16 | comment | added | Szabolcs |
Here's how to create a HistogramDistribution from already binned data.
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| Aug 22, 2017 at 1:10 | answer | added | Edmund | timeline score: 6 | |
| Aug 18, 2017 at 19:45 | comment | added | astrsk | we do not have any pre-set distribution. Just counted hist. data, from which I infer some distribution. A particular problem is comparing data with, e.g., normal dist (null hypothesis that data come from it). But the first step is to build density distribution (bins are unequal, but distributed close to logarithm, like: 200-300-500-1000-2000-3000- >3000). So it is to build true density hist. adjusting bar heights, and then via kernel smoothing infer a nonparametric estimation of the distribution. HistogramDistribution seems to do this for raw data, the same is for SmoothKernelDistribution | |
| Aug 18, 2017 at 17:34 | comment | added | JimB | Do you have a specific distribution in mind? Or is this just for any general histogram without a specific distribution in mind? I ask because if one has the histogram (counts and bin structure) and that the distribution is, say, a normal distribution, then there is an approach to obtain maximum likelihood estimates of the two parameters (mean and standard deviation in this case) that is very different than pretending a uniform distribution within a bin. | |
| Aug 18, 2017 at 17:03 | history | edited | m_goldberg | CC BY-SA 3.0 |
Made English more idiomatic
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| Aug 18, 2017 at 16:35 | comment | added | J. M.'s missing motivation♦ |
Have you seen HistogramDistribution[] and SmoothKernelDistribution[]?
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| Aug 18, 2017 at 16:29 | history | asked | astrsk | CC BY-SA 3.0 |