Found the answer A PDF answers the question: "How common are samples at exactly this value?" A CDF answers the question "How common are samples that are less than or equal to this value?" The CDF is the integral of the PDF.
Since PDFs model Continuous Random Variables (CRV), they cannot give the probability of a particular value. They can only give the probability of a particular interval. So your statement should be rephrased as: A PDF answers the question: "How common are samples at this interval?"
PMF finds the probability of being a sample at an exact value, but ONLY if we are in the discrete realm. There is no such thing as "probability at exactly this value" in the continuous realm because exact values are Infinitesimally small (area of something with no width = 0), hence why we use PDF to answer it terms of densities or intervals. Also, the y axis of the PDF graph is NOT the probability.
The best explanation that I see. I am doing the course of MIT about proability, but your explanation is very clear and simply. Good job guys. Go on!!! Best wishes from Uruguay!!!
It is a very good explanation but i am still not clear why you need PDF if you can calculate probabilities from CDF? Which one has a better algorithmic efficiency?
I think you guys completely swapped the PDF and CDF. The explanations and everything make sense, but the CDF is the area under the curve. Hence the name Cumulative distributive function. PDF assigns specific values to inputs, which is what you guys did on the right hand side.
Found the answer
A PDF answers the question: "How common are samples at exactly this value?" A CDF answers the question "How common are samples that are less than or equal to this value?" The CDF is the integral of the PDF.
THanks .... best explanation of CDF and PDF
Thanks so much omg
Since PDFs model Continuous Random Variables (CRV), they cannot give the probability of a particular value. They can only give the probability of a particular interval.
So your statement should be rephrased as: A PDF answers the question: "How common are samples at this interval?"
PMF finds the probability of being a sample at an exact value, but ONLY if we are in the discrete realm. There is no such thing as "probability at exactly this value" in the continuous realm because exact values are Infinitesimally small (area of something with no width = 0), hence why we use PDF to answer it terms of densities or intervals. Also, the y axis of the PDF graph is NOT the probability.
After almost an entire day of struggling, your video saved me! Thank you!
Thank God for these two virgins. They saved my day. Thank you so much!
Damn crystal clear! I had been struggling on the concept of the difference between pdf and cdf for days until i saw this clip. Thanks guys!
best explanation 📚
If you guys were comedians, this video would be even better. Thanks for the help!
Thank you so much for the clear explanation! You guys make the PDF and CDF look much easier to me.
The best explanation that I see. I am doing the course of MIT about proability, but your explanation is very clear and simply. Good job guys. Go on!!! Best wishes from Uruguay!!!
Thanks to you guys for making my Master's degree seem like a waste of money :)
Very simple and concise way of explaining the difference between the two.
I love math but I'm not good at it
This is so good, two guys just enjoyinh maths, you rock!
Thank you so much!! Good beginner's introduction to develop an intuition of what it is!
Awesome video
Amazing video, super clear explanation. Thank you very much
Love the simplicity in the explanation. Keep it up!
Bahut achchhe sir.keep it up.🙏
Excellent explanation brothers!
Thank you, very much appreciated - The explanation is clear as water.
to put it simply, the CDF is the indefinite integral of the PDF.
*definite
this is amazing pls make more videos like this
Amazing
asheesh explained it way better then that PAB
facts
?
Jyshhfgxg
Thank you so much for explaining this!!
Awesome, you guys crushed it. Just what I was looking for.
Okay,this is kinda easy to understand.
p.s watch it thrice lmao
and also,Ashish explained it well
nyc explaination guys
Short, Simple, and to the point. Good video. Hope there's more.
in short, pdf is the derivative of the cdf
@Aaron McGill haha you're not wrong
Great explanation!
This video was very helpful! Thank you!
Awesome video! Thank you!
Thank you so freaking much to the both of you!
Simple and easy to understand... Thanks.
I prefer the comments to the video
Perfect explanation! Thanks
thanks. finally found an tutorial that is easy to understand
Oh man! That ending was awesome!
Best explanation thank you guys.
Cool
Nice guys but need to make a complete video on cdf ....
Hashish, got some more of that hashish?
ههههااي حلوة
Nice one 😂
Its 'Ashish' BTW
well explained
Love it, crystal clear
my man ashish showing that PAB how its done
amazing, straight to the point, ty
Perfect
Helped clear things out thanks
1) that was easy.
2)I expected hasish to have a completely different voice lmao
3)Why was hasish yelling at me lol
Ez Pz its ashish😂😂
@@kenilvora9573 ashish is his name, hashish is what he smokes
@@robertwanko219 SHIT THAT MADE ME LAUGH LIKE A MORON
@@robertwanko219 lmao thats funny
hasish you are one sick guy
Its the height (CDF) vs area (PDF) concisely
It is a very good explanation but i am still not clear why you need PDF if you can calculate probabilities from CDF? Which one has a better algorithmic efficiency?
Ashish needs to work on his posture
great job, guys! Thanks
This video is great. Thanks!
wait isnt the equation to calculate CDF is integrate PDF from negative infinity to x? so why the derivative of PDF is CDF?
The derivative of cdf is pdf :)
love your ending animation haha
thank you! this helped a lot :)
PDF is the derivative of CDF. F'(x)=f(x)
Is he ashish agrawal??
way to explain integration without calculus
simple and helpful THX!!
thanks from Italy! xx
Fk I luv this two guy
Thanks!
thanks guys that was helpful
Oh, great :)
Great. ty guys
is PDF just the integral of CDF?
Nope ,Pdf is the derivative of cdf
Dude's name is Hashish.
no, its Ashish
trying to get the same answer
thank you, this was helpful
Stop rushing Ashish Dude. Let him explain himself and what is up with writing over his area. Slow down because you confused the hell out of me.
So its an integral... Could have saved a bunch of time there...
second guy killed the explanation (killed = good thing lol) thanks
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Well! How to get even more confused!!
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In summary... differentiating PDF gives you CDF... helpful video nonetheless!
wait isnt the equation to calculate CDF is integrate PDF from negative infinity to x? so why the derivative of PDF is CDF?
+Jianxiong Ji good point. Typo, I should switch those two terms. Sorry for the confusion.
***** for sure bro
I think you guys completely swapped the PDF and CDF.
The explanations and everything make sense, but the CDF is the area under the curve. Hence the name Cumulative distributive function. PDF assigns specific values to inputs, which is what you guys did on the right hand side.
Noah McCollum-Gahley no it is correct. PDF is area under the graph, you have to integrate the function.
Clark Amy Davis Laura Johnson Barbara
I hate maths -_-
Thank you so much. Satistics is such a bullshit form of mathematics.
Trigonometry is better.
Lol. nerds
that was a terrible explanation, i learnt nothing
same here got me nowhere
haha yeah!
Murazik Parks