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 Get underlying function from Kernel Density Estimation jpython Unladen Swallow Posts: 4 Threads: 2 Joined: Dec 2019 Reputation: 0 Likes received: 0 #1 Dec-04-2019, 11:02 PM 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 scidam  Posts: 668 Threads: 1 Joined: Mar 2018 Reputation: 90 Likes received: 103 #2 Dec-04-2019, 11:51 PM (This post was last modified: Dec-04-2019, 11:53 PM by scidam. Edited 2 times in total. Edit Reason: clarifications added ) 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). jpython Unladen Swallow Posts: 4 Threads: 2 Joined: Dec 2019 Reputation: 0 Likes received: 0 #3 Dec-05-2019, 08:22 AM (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? jpython Unladen Swallow Posts: 4 Threads: 2 Joined: Dec 2019 Reputation: 0 Likes received: 0 #4 Dec-05-2019, 11:23 AM (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. « Next Oldest | Next Newest »

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