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excellent lecture

It would be better to solve more examples than just explaining throughout the entire video. I didn't register a thing... Try being like organic chemistry tutor.

These explanations are absolutely fantastic! They are crystal clear and easy to understand. I was blown away by how brilliantly they were presented 👌 Thank you very much 🙏

Great explanation.Thanks a lot professor...

Thank you Geoffrey for such an amazing content, I'm learning quite fast with your videos. Greetings from Medellin, Colombia !

Thank you, that is so useful

Excellent

Thank you from vietnam

thank you ! very clear explication !

First of all, thank you very much for sharing this playlist. I'm eager to consume it. But one critic about the notation of the null set. If you write {∅}, in terms of set notation, this can only mean the set containing the null set, and in this case this set in turn is not empty. Maybe it is advisable to only use ∅ or {} for the empty set.

Thank you, professor.

thank you for this video

At the min 12:10 you don't explain what N0 is.

As the material becomes progressively harder, the explanation becomes less intuitive and feels a bit rushed. Miss the time when you spent more than 15 mins to explain something as simple as Baye's theorem with real-life examples.

*Commenting to remember this series for this semester

It is so weird to me that Parseval's theorem holds. I can maybe understand that both integrals should be finite... but that the frequency integral should be equal to the energy ... that's weird!

Thank you so much for this :) Any chance we could have access to the slides?

thank you so much

Dear Professor,can you please upload the slides or provide link to download?Thanks

I think in slide 4, instead of n tends to infinity, it must be nA tends to infinity (that is nA becomes large) because as n tends to infinity the limit approaches zero, i.e P(A) approaches zero & example given of an infinite number of coin tosses, probability of head will not be half, instead, it will be zero.

Greetings Professor. Please, is it possible to get a link to your slides? I'd be much grateful. Thanks alot.

This is helping me in my stats class this semester. Thanks so much.

Finally someone's approach which can accepted by my head. Such a clear and concise way to present the concept. Thanks for a straightforward explanations.

thanks

28:15 life in short 🔥

why is the prob of the user getting the failed product not 0.99 as if g1 does not fail there is only a 1% chance that g2 will fail and the user will get the damaged product

Great descriptions! My professor did not do nearly as good a job describing these concepts in plain language.Thank you for putting these online!

thank you so much sir for sharing these lecture videos.

Hi, professor, I think the number of Module 5 and 6 should be switched.

For anyone not clear on where the matrix at 21:47 comes from: π = πP π I = πP (the I is the identity matrix) 0 = πP - π I (the 0 is a zero vector) Since the new equation is (without the 1s at the bottom row): 0 = Aπ A = (P - I)^T (you need to transpose to get the π on the right side of the A)

I am so lucky to know the field by this 1st video. So clear and clever way to give the information, thank you. 🌹🌹🌹

I am really glad that I came across this playlist.

i clicked on this looking for something else and i am so thankful i did not have to learn this

14:01 I'm pretty sure it should be d^2 F(...) / d x_1 d x_2 instead of d F(...) / d x_1 d x_2

thank you sir for explaining joint function RV and its characteristics. because of people like you who provide free quality education, world became a livable place thank you again, sir. you achieve the success that you want sir.

If a trial is a single roll of a dice or flip of a coin, does that mean that trial should be defined as: "Performing an experiment that generates a single elementary event" rather than "...a single event"?

Thank you for the course it is very helpful ❤ god bless you

Thank you, really great job. I am trying to get this A for the Fall(2020)

15:20 PSD S(f) = E\{ \phi_{P,T}(f) \} = E\{ 1/T * | \phi_T(f) |^2 \}, right? IOW, PSD is equal to the expected value of the power density (ϕ sub-indexed by both P and T), which therefore should be equal to the expected value of the magnitude squared of the power spectrum (ϕ sub-indexed by T only) multiplied by 1/T

Thanks a lot Prof. Messier.

Thank you professor It helped me a lot... I was struggling with this concept for a while but finally I get to know about the concept

this video can reasonably be done in 5 minutes but your bullshits take up the rest.

Too much bullshit in your words, I totally get astray. Shame.

i like this very much / i am german but most german Videos on stachastics are not reallz well understandable

Thank you so much.

Hi Dr. Messier, Is the domain of a Random Variable lkimited to the real-valued set or can it be complex-valued?

Great explanation ! very clear this helps me alot thank you!! your students must be lucky

Excellent

profe como puedo mejorar una modulación OFDM?

excellent explanation, thank you. Can someone please tell me where the filter in f-OFDM would be put in the OFDM system block diagram (34:07)?