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User1865345
User1865345
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I was looking for an answer to this bandwidth matrix optimisation problem and I found this excellent other thread, so I thought I'd drop it here. :

https://stackoverflow.com/questions/67189978/difference-in-bandwidth-for-scikitDifference in bandwidth for scikit-learn-kde-and-multivariate-kde-of-statsmodels KDE and multivariate KDE of statsmodels.

In short, it says that the sklearn KernelDensity() implementation uses bandwidth as a multiplier of the diagonal matrix (so second case of Tim's answer), while statsmodel's KDEMultivariate() estimates different multipliers (so third picture, I believe). I am not sure how this compares to scipy which multiplies the covariance matrix by the single scalar. It looks to fall in the same case as KDEMultivariate(), but with a little less control over the dimension-specific toggling. From what I understand (again from that other stackoverflow answer), they both use rule of thumb for coming up with the covariance matrix.

I was looking for an answer to this bandwidth matrix optimisation problem and I found this excellent other thread, so I thought I'd drop it here. https://stackoverflow.com/questions/67189978/difference-in-bandwidth-for-scikit-learn-kde-and-multivariate-kde-of-statsmodels

In short, it says that the sklearn KernelDensity() implementation uses bandwidth as a multiplier of the diagonal matrix (so second case of Tim's answer), while statsmodel's KDEMultivariate() estimates different multipliers (so third picture, I believe). I am not sure how this compares to scipy which multiplies the covariance matrix by the single scalar. It looks to fall in the same case as KDEMultivariate(), but with a little less control over the dimension-specific toggling. From what I understand (again from that other stackoverflow answer), they both use rule of thumb for coming up with the covariance matrix.

I was looking for an answer to this bandwidth matrix optimisation problem and I found this excellent other thread, so I thought I'd drop it here:

Difference in bandwidth for scikit-learn KDE and multivariate KDE of statsmodels.

In short, it says that the sklearn KernelDensity() implementation uses bandwidth as a multiplier of the diagonal matrix (so second case of Tim's answer), while statsmodel's KDEMultivariate() estimates different multipliers (so third picture, I believe). I am not sure how this compares to scipy which multiplies the covariance matrix by the single scalar. It looks to fall in the same case as KDEMultivariate(), but with a little less control over the dimension-specific toggling. From what I understand (again from that other stackoverflow answer), they both use rule of thumb for coming up with the covariance matrix.

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Magi
Magi
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I was looking for an answer to this bandwidth matrix optimisation problem and I found this excellent other thread, so I thought I'd drop it here. https://stackoverflow.com/questions/67189978/difference-in-bandwidth-for-scikit-learn-kde-and-multivariate-kde-of-statsmodels

In short, it says that the sklearn KernelDensity() implementation uses bandwidth as a multiplier of the diagonal matrix (so second case of Tim's answer), while statsmodel's KDEMultivariate() estimates different multipliers (so third picture, I believe). I am not sure how this compares to scipy which multiplies the covariance matrix by the single scalar. It looks to fall in the same case as KDEMultivariate(), but with a little less control over the dimension-specific toggling. From what I understand (again from that other stackoverflow answer), they both use rule of thumb for coming up with the covariance matrix.

I was looking for an answer to this bandwidth matrix optimisation problem and I found this excellent other thread, so I thought I'd drop it here. https://stackoverflow.com/questions/67189978/difference-in-bandwidth-for-scikit-learn-kde-and-multivariate-kde-of-statsmodels

In short, it says that the sklearn KernelDensity() implementation uses bandwidth as a multiplier of the diagonal matrix (so second case of Tim's answer), while statsmodel's KDEMultivariate() estimates different multipliers (so third picture, I believe). I am not sure how this compares to scipy which multiplies the covariance matrix by the single scalar.

I was looking for an answer to this bandwidth matrix optimisation problem and I found this excellent other thread, so I thought I'd drop it here. https://stackoverflow.com/questions/67189978/difference-in-bandwidth-for-scikit-learn-kde-and-multivariate-kde-of-statsmodels

In short, it says that the sklearn KernelDensity() implementation uses bandwidth as a multiplier of the diagonal matrix (so second case of Tim's answer), while statsmodel's KDEMultivariate() estimates different multipliers (so third picture, I believe). I am not sure how this compares to scipy which multiplies the covariance matrix by the single scalar. It looks to fall in the same case as KDEMultivariate(), but with a little less control over the dimension-specific toggling. From what I understand (again from that other stackoverflow answer), they both use rule of thumb for coming up with the covariance matrix.

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Magi
Magi
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I was looking for an answer to this bandwidth matrix optimisation problem and I found this excellent other thread, so I thought I'd drop it here. https://stackoverflow.com/questions/67189978/difference-in-bandwidth-for-scikit-learn-kde-and-multivariate-kde-of-statsmodels

In short, it says that the sklearn KernelDensity() implementation uses bandwidth as a multiplier of the diagonal matrix (so second case of Tim's answer), while statsmodel's KDEMultivariate() estimates different multipliers (so third picture, I believe). I am not sure how this compares to scipy which multiplies the covariance matrix by the single scalar.