Kernel Density Estimation with Seaton Ullberg
Rustacean Station
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The KDE Technique Is Really Critical to Getting Accurate Estimates, Right?
When you do have more dimensions, you definitely need more sampling, right? So sampling deficit just means more actual data to plug into your algorithm. But I think something to keep in mind too is what about noise or what I call noise. And that kind of brings in another piece of the puzzle, the KDE technique,. A part of it that is actually super critical to getting accurate estimates are a term known as bandwidth.
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