Inverse Transform Sampling - VISUALLY EXPLAINED with EXAMPLES!

This tutorial explains the Inverse Transform Sampling using a simple example. The proof of why the algorithm/transform works is also explained.

#sampling
#statistics
#distributions

[cisanta]

Beautifully explained!! Inverse transform was such an abstract concept for me until now. Walking thru the proof is helping me grasp the concept so much better!

[ohnekojian]

This is the best explanation with all the nice prepared visual on the very important idea of Inverse sampling. Thanks a million.

[GITARRobErt]

Thank You a lot!!! This is the best presentation of the proof I have seen so far after watching a lot of other videos and papers about the universality of the uniform!! :)

[skperera-gl]

Excellent video with clear explanation. Thanks!

[hossainshaikhani]

This was a clear and concise explanation of the method. Thank you.

[tobe]

I think you inverse « this is what we know » and « this is what we’re proving »
Great and clear video. Thanks

[mikenim]

Bravo Bravo! This is absolutely fantastic! Thanks for sharing

[operonandonandon]

Wonderful, clear explanation! Thank you for sharing this video.

[antonglushchenko]

Thank you very much for great explanation. Clear and understandable video. The proof part was right in time too 🍾

[BluBr]

5:57 gave me the aha!-moment. Thank you so much!

[martinsanchez-hwfi]

Good video. In Minute 10 you say cdf of Uniform is 1. That is for the pdf.

[parvingh]

Thank you so much. Can I know which program do you use for your present? Whats name of it?

[AhmedMohamed-ddef]

Question : Hi, i have rainfall data as a 2d matix/frame of the UK every 5 minutes so the data is spatially and temporarily correlated. The data has severely positive skewness. Around 90% of pixels or points are less than 10 and 10% between 10-128. When i train a cnn, it is only predict rainfall of low values because of the data imbalance. I would like to transform to uniform distribution. I tried log transformation which compressed the data but still there is imbalance. Do you know how to convert to a uniform distribution so all of the values have the same chance to be predicted? It is a regressio task to predict the next 12 frames of rainfall. The data is represented by only one continuous variable, rainfall intensity. Many thanks

[addisonweatherhead]

The statement at 9:53 doesn't seem right to me. If Y is a uniform on [0, 2], then the density at every point is 0.5 (since 0.5*2=1). The CDF evaluated at 1.5 is simply 1.5*(.5) = 0.75. But doesn't the statement at 9:53 state that the CDF evaluated at a point (1.5) should be equal to that point (1.5)?

[blasttrash]

is this better than box mueller or ziggurat algorithm in terms of speed while implementing in code?

[MDNQ-udty]

I was trying to prove that F_X(X) is U(0, 1) from first principles. That is, without taking inverses. Seems to be a little harder than I thought ;/

[bodwiser]

Thanks. Could you explain what is "x" and what is "samples"?

[musiknation]

Sir can you show this in R for simulation of samples

[abdulwasaye]

Still you didn’t explain the need of CPDF, also you told that we want to get rid of evaluating integral therefore we want something…? What CPDF, but CPDF is integral of pdf