Timeline for Deciding which points to be used for density estimation
Current License: CC BY-SA 3.0
5 events
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Dec 18, 2017 at 17:24 | comment | added | Miguel | Intuitively, I'd say that if your target distribution doesn't have fat tails, that is a good ansatz, but I don't have any theory to back that up. I guess you could try different strategies and cross-validate them across as much data as you can get your hands on. | |
Dec 18, 2017 at 16:38 | comment | added | llxxee | Thank you, kernel density estimation solves the first part of my question, but when we can choose which points to observe, how do we make the decision? Say we know all the data frequency in a data set $f_1,f_2,...f_n$, but we can only observe $k$ points in the dataset to do the density estimation, what should be our strategy? Should we choose the data points with the highest frequency? | |
Dec 18, 2017 at 15:05 | history | edited | Miguel | CC BY-SA 3.0 |
Mention histograms
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Dec 18, 2017 at 14:22 | history | notice added | Tim | Needs detailed answers | |
Dec 18, 2017 at 14:14 | history | answered | Miguel | CC BY-SA 3.0 |