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Practical to follow, thanks.

Why we need "smaller" sigma algebras and not use always powerset as sigma algebra?

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Man, 12 years ago you did a better job than recent articles and lessons. I swear I understood the algorithm in less than 5 minutes, which I'm looking for descriptive contents to understand for days. I can't thank you enough, I'm trying to write a new implementation of an image format! If I become Linus Torvalds someday, I'll make sure people will now you helped me a lot haha!

why do we need the probability mass function? wouldn't the steps be the same for encoding and decoding for an even distribution?

very nice video. thank you so much!

Hello ,I have one question,why are mu and theta not hidden variables?

I pressed the like button only for the sounds you created while drawing the lines

The whole series of the lectures are so great and I like them very very much! As you mentioned in one of your lecture, there are three big theorems in information theory: source coding theorem, rate distortion theorem and channel coding theorem. It is my best wish that you can provide video lectures for the contents of the channel coding and rate distortion theorems. Thank you so much for the wonderful lectures!

I have returned to this derivation several times over that past many years. This is a clear calculation of the gradient of the log likelihood function for logistic regression. Thank you!

if C_j is a vector of counts of each category, what does alpha mean?

Sir your tutor is amazing. But many students are not benefiting from it. So for you to help them find your tutor please first add profile to your youtube channel and for your videos add thumbnails on it.

This is like the best algorithm ever designed. So many nice properties, from being able to derive the length of the source string from the interval while decoding, to this elegant entropy property. Utterly beautiful.

You had me at “this is overhead”. Number version is bloat, just like Microsoft telemetry

Sorry the way you pronounce theta and data really confuses me 😶‍🌫

this is so good man

why is the differential equal to the term inside the infinte sum?

Just one example with real values and data would have helped alot

Fantastic presentation, easy to follow and giving great intuition, thank you.

It's videos like these that make you wish youtube had a 3x playback speed...

The first column of the desing matrix should contain only 1s, shouldnt it?

What did he say

super intuitive Thanks

Why B=2?? In example a!

Hello, I have watched the whole series of lectures about arithmetic encoding and I'm very thrilled about this algorithm and your way of explaining things. You are a great professor and mathematician. Thank you for your lectures! Unfortunately my finite precision implementation works incorrectly on large input data (maybe due to round off error). So maybe you cold share your implementation of the algorithm. It would be a great help! Thank you in advance!

It was one of the best explanations, so informative and helpful. Thank you!

Your enthusiastic way of teaching is so inspiring. Thank you for sharing this great video!

With regrets I have to say, these are hardly good videos to learn this topic from. It is not the first video that makes me completely confused about where things come from or why certain terms appear in the main or side equations. It's not well organised IMO. I have made a few attempts to come back to the playlist and give it a another go. First I thought I was missing basics. Now, I am convinced the videos are problems. The idea is great, the effort is still appreciated, but sadly it's hard to notice or understand things if they are not explained.

You cite a study but you do not cite the inventor of Random Forests, Leo Breiman.

I like these videos but this one was very confusing!

yes this seems good, i agree on that topic

ありがとう、先生👍

Good explanation of Poisson and Exponential rv

I strongly disagree about the naming you are talking at the beginning. Logistic regression ultimately serves as a classification method, but it fits a logistic (or sigmoid) line to the data so it could be thought as a regression process.

Didn't quite follow the step where "the joint was summed over c". What does that mean?

A little dissapointed that Arianna Rosenbluth wasn't included in the list of creators. She was also the first to use the method. Great explanation of MCMC!

I like this name "mathematicalmonk" a lot. Because I think you must have a monk's heart to love mathematics...

thank you

Speaking from personal experience, you explain these concepts so much better than some professor with an h-index of 60 at a well-known university.

sir, may you please make a video for gamma random variables expected values

It was perfect exposition of the subject many thanks

This vid makes references to watching other vids before proceeding to the next one !

I must admit, I got lost in the details here ... 😢

for the continuous case, of what use is it?

Can you please come back to youtube sir, your videos are great. Sadly im 10 years late to see your videos

thanks for ur efforts

Thanks for the video, but in fact you don't need to factor both C and D, just factoring one of them is enough! As Schur did in his paper (1911).

Thanks so much, after looking at many explanation on this topic, I finally got it after watching this video.

In the shell game X and Y are not independent like in your equations. In the shell game there us an additional equation: X + Y + Z = 1.

@mathematicalmonk at 1:26 you write P(A, B, C) =P(A|C) P(B|C) P(C). How do you prove it?