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KDE method more sensitive to multimodality
The Next CEO of Stack OverflowDifference between append vs. extend list methods in PythonUnderstanding Python super() with __init__() methodsStatic methods in Python?Does Python have a string 'contains' substring method?KDE fails with two points?How to estimate density function and calculate its peaks?Kernel Density Estimation in PythonPlot curves instead of bar plot using pythonResampling a KDE (Kernel density estimation) in statsmodelsHow to identify the modes in a (multimodal) continuous variable
I am using scipy's stats.gaussian_kde() to produce a PDF estimate for a sample of data.
Scipy's docs clearly state:
"The estimation works best for a unimodal distribution; bimodal or multi-modal distributions tend to be oversmoothed."
Is there a method that may be more sensitive to spikes in frequency that does not involve manually setting bandwidth?
My assumption is that, because it is non-parametric, Gaussian KDE does not assume the shape of the distribution-- yet it seems to be forced to assume normality nonetheless.
python scipy kernel-density probability-density
asked Mar 21 at 17:04
UBHDNNXUBHDNNX
217
add a comment |
I am using scipy's stats.gaussian_kde() to produce a PDF estimate for a sample of data.
Scipy's docs clearly state:
"The estimation works best for a unimodal distribution; bimodal or multi-modal distributions tend to be oversmoothed."
Is there a method that may be more sensitive to spikes in frequency that does not involve manually setting bandwidth?
My assumption is that, because it is non-parametric, Gaussian KDE does not assume the shape of the distribution-- yet it seems to be forced to assume normality nonetheless.
python scipy kernel-density probability-density
asked Mar 21 at 17:04
UBHDNNXUBHDNNX
217
add a comment |
I am using scipy's stats.gaussian_kde() to produce a PDF estimate for a sample of data.
Scipy's docs clearly state:
"The estimation works best for a unimodal distribution; bimodal or multi-modal distributions tend to be oversmoothed."
Is there a method that may be more sensitive to spikes in frequency that does not involve manually setting bandwidth?
My assumption is that, because it is non-parametric, Gaussian KDE does not assume the shape of the distribution-- yet it seems to be forced to assume normality nonetheless.
python scipy kernel-density probability-density
asked Mar 21 at 17:04
UBHDNNXUBHDNNX
217
I am using scipy's stats.gaussian_kde() to produce a PDF estimate for a sample of data.
Scipy's docs clearly state:
"The estimation works best for a unimodal distribution; bimodal or multi-modal distributions tend to be oversmoothed."
Is there a method that may be more sensitive to spikes in frequency that does not involve manually setting bandwidth?
My assumption is that, because it is non-parametric, Gaussian KDE does not assume the shape of the distribution-- yet it seems to be forced to assume normality nonetheless.
python scipy kernel-density probability-density
python scipy kernel-density probability-density
asked Mar 21 at 17:04
UBHDNNXUBHDNNX
217
asked Mar 21 at 17:04
UBHDNNXUBHDNNX
217
asked Mar 21 at 17:04
UBHDNNXUBHDNNX
217
asked Mar 21 at 17:04
UBHDNNXUBHDNNX
217
asked Mar 21 at 17:04
UBHDNNXUBHDNNX
217
217
add a comment |
add a comment |
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