What is a Cumulative Distribution Function (CDF) of a Random Variable?

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Iain Explains Signals, Systems, and Digital Comms

3 жыл бұрын

Explains the Cumulative Distribution Function (CDF) of a Random Variable using examples of the uniform distribution and the Gaussian distribution.
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For a full list of Videos and accompanying Summary Sheets, see the associated website: www.iaincollings.com

Пікірлер: 32

@aezazi 2 жыл бұрын

Thanks so much for this. I have a MS in Engineering, but that was decades ago. At age 60, I decided to essentially go back to school and Learn AI and ML, so having to learn and re-learn this material. This explanation of CDF is extremely illuminating. I now understand the concept better than I did back in graduate school!

@iain_explains 2 жыл бұрын

Thanks so much for letting me know that you found the video helpful. Comments like these really do help to motivate me to make more videos. I'm so glad to be able to help not just current university students, but anyone interested in these topics.

@okocha979 2 жыл бұрын

Very simple yet very understandable explanations. Thanks in advance for your future videos.

@iain_explains 2 жыл бұрын

Glad it was helpful!

@himanshu1056 2 жыл бұрын

Great one Iain. Thanks for keeping us on momentum.

@iain_explains 2 жыл бұрын

My pleasure

@user-wv5yp2by1o 6 ай бұрын

Extremely well explained. Concise and clear

@iain_explains 6 ай бұрын

I'm glad you liked it.

@MarcosMynssem 3 жыл бұрын

your videos are awesome. please don't stop doing it

@iain_explains 3 жыл бұрын

Thanks. Glad you like them!

@archana9504 Жыл бұрын

Grateful that i found this playlist 🥳🥳

@iain_explains Жыл бұрын

Glad you like it!

@Amine1z 3 жыл бұрын

Please keep making great videos like this

@iain_explains 3 жыл бұрын

Thanks. That's the plan!

@user-vx3jm1vi6p Жыл бұрын

Thank you so much, Professor.

@iain_explains Жыл бұрын

You're welcome.

@woltedjidereje8609 2 жыл бұрын

Thank you incredibly much

@iain_explains 2 жыл бұрын

You're very welcome!

@algebra7427 2 жыл бұрын

Could we have derived the expression stated at 5:55 by taking the definite integral of the function f(theta). That is, take the definite integral of the uniform distribution (PDF) on the left and integrate between x2 and x1 as our upper and lower bounds respectively? Thanks for the excellent content!

@iain_explains 2 жыл бұрын

Yes, that's right.

@bishalpoudel3859 2 жыл бұрын

nice explanation sir

@iain_explains 2 жыл бұрын

Thanks for liking

@Learnwithme.07 2 жыл бұрын

Do you have videos on adaptive filtering? I can't find it on your channel.

@iain_explains 2 жыл бұрын

Thanks for the suggestion. I've got adaptive filters on my "to do" list. Next week's video is on the Least Squares estimate, so keep an eye out for that.

@rationalthinker9612 Жыл бұрын

Crystal clear explanation but there's one thing I'm confused on. I'm an EE student taking a Random Signals Analysis class and the textbook section that talks about PDF and CDF states that only discrete random variables have PMF's. In your example, are the functions discrete or continuous? They are drawn like they're continuous so that's my confusion.

@iain_explains Жыл бұрын

I've drawn functions that relate to continuous random variables. Because the RV is continuous, it means the probability function is a "density" function. If the RV was discrete, then the probability function would be a "mass" function. I think I might make a video on this point, because I suspect others may have the same confusion.