Get underlying function from Kernel Density Estimation - Printable Version +- Python Forum (https://python-forum.io) +-- Forum: Python Coding (https://python-forum.io/Forum-Python-Coding) +--- Forum: Data Science (https://python-forum.io/Forum-Data-Science) +--- Thread: Get underlying function from Kernel Density Estimation (/Thread-Get-underlying-function-from-Kernel-Density-Estimation) Get underlying function from Kernel Density Estimation - jpython - Dec-04-2019 Hi everyone, There are several libraries that allow us to estimate a probability density function using Kerndel Density Estimation. My question is how I can see the estimated function, not as a plot but as a formula/equation. I hope you understand what I mean. Best regards, jpython RE: Get underlying function from Kernel Density Estimation - scidam - Dec-04-2019 There is no simple representation of such formula. You are likely looking for a simple one. You can find explanation about kde-estimations at Wikipedia. So, kde-estimation is a function defined as a sequence of calculations (these calculations are hard to perform manually, but easy to do with help of the computer), it is a computational procedure. If you want to get a "convenient" formula as an estimation of pdf, you can look at parametric probability density estimations, e.g. fit a normal distribution, or fit a mixture of normal distributions (using mixture of known distributions might be very flexible, if you want to handle non-normally distributed data). RE: Get underlying function from Kernel Density Estimation - jpython - Dec-05-2019 (Dec-04-2019, 11:51 PM)scidam Wrote: There is no simple representation of such formula. You are likely looking for a simple one. You can find explanation about kde-estimations at Wikipedia. So, kde-estimation is a function defined as a sequence of calculations (these calculations are hard to perform manually, but easy to do with help of the computer), it is a computational procedure. If you want to get a "convenient" formula as an estimation of pdf, you can look at parametric probability density estimations, e.g. fit a normal distribution, or fit a mixture of normal distributions (using mixture of known distributions might be very flexible, if you want to handle non-normally distributed data). Hi Scidam, I am able to use the methods you mentioned, but only to evaluate the the probability density function at a certain point. I woul like the formula in mathematical notation. Is this possible? RE: Get underlying function from Kernel Density Estimation - jpython - Dec-05-2019 (Dec-05-2019, 08:22 AM)jpython Wrote: (Dec-04-2019, 11:51 PM)scidam Wrote: There is no simple representation of such formula. You are likely looking for a simple one. You can find explanation about kde-estimations at Wikipedia. So, kde-estimation is a function defined as a sequence of calculations (these calculations are hard to perform manually, but easy to do with help of the computer), it is a computational procedure. If you want to get a "convenient" formula as an estimation of pdf, you can look at parametric probability density estimations, e.g. fit a normal distribution, or fit a mixture of normal distributions (using mixture of known distributions might be very flexible, if you want to handle non-normally distributed data). Hi Scidam, I am able to use the methods you mentioned, but only to evaluate the the probability density function at a certain point. I woul like the formula in mathematical notation. Is this possible? My apologies, I did not read your post carefully. I did look briefly into parametric probability density estimations but again I was not able to print the underlying function it uses to do the estimation.