Inverse Transform Sampling + R Demo

Using the inverse transform method to get random samples from a non-uniform distribution.
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//Chapters
0:00 Inverse transform sampling explanation
2:22 Example
6:18 R demo
//Second Inverse Transform video
• Inverse Transform Samp...
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[@muhannadobeidat]

Great video, pace and example. Thanks a million

[@user-ki1nl5qk1x]

amazing video! informative and straight to the point explanation with a great example

[@Hailey-vg9jz]

Thanks for saving me. I already ruined my midterm but thanks to you I've got hope for my final.

[@homataha5626]

I loved it!

[@bogusmcbogus2637]

Fantastic explanation. Subscribed.

[@angienicolehernandez104]

Thanks! I finally get it!

[@338Maxime]

God that was amazing, thank you.

[@jgeff9777]

You're a legend

[@myzafran1]

Thank you!!!!

[@accountantguy2]

Great explanation! I finally "get it".

[@vankadavathrohith1589]

Great video, tq so much

[@richardren9869]

thanks you saved my life

[@maximegrossman2146]

excellent

[@fntldks]

FINALLY get it, you explained how it works!! it is cool!

[@Lovestrant]

I got it.Thanks

[@Silver1980love]

Great. Deserve 5 🌟

[@vaibhavghadge4057]

Thanks

[@sergiotahim629]

great video, and if a need to transform a normal distribution to a uniform distibution?

[@sindbadthesailor1541]

Could you explain the concept of copulae?

[@yt-1161]

When integrating choose a variable different from x

[@onstantinos.7286]

[@kerguule]

If I would now do some Monte Carlo simulation with inverse transform sampling so that I got a group of failure times as an output, do I already see the expected outcome if I plotted the PDF? So that the higher the Y-axis value peak the more failure times around that time (X-axis) I would expect? The PDF of that exponential function is a decreasing curve but the hazard rate is constant. Why do we call that memoryless even though we would get more failure time values according to the PDF in the beginning (because it looks to be a decreasing curve)?

[@adetunjiademola498]

What happens when there's no definite form for the transformed variable?
I mean when X can't be made subject of the formula in a definite form

[@saragreesa2839]

If it is normal distribution how to transform?

[@tomatorama]

Hi, I’m curious, why is there a need to get non uniform samples from uniform sampling ?

[@ejovo]

The true need is "How can we generate a random non-uniform sample?".
You might need to do that if you want to perform simulations of... literally anything. How a pandemic spreads. How traffic flows in urban planning.
We've developped mathematical tricks to generate a (pseudo) random uniform distribution, and it turns out we can use the uniform distribution to generate any other non-uniform distribution!
Look at the fact that we generate a non uniform from a uniform distribution as a neat trick rather than necessity. We _need_ to generate a non uniform sample and using the uniform with the inverse transform of the cdf is a convenient way to do so.