Your channel is criminally underviewed. You are a really talented lecturer. I took a whole bayesian stats course and you revealed things already I didnt learn in 5 months of time.

This is how teaching should be done. It was incredibly effortless to understand when you switch between intuition and rigor. Thank you so much

bro, you are the absolute best in explaining complicated concepts in such an intuitive manner. You should seriously consider becoming a teacher since you are so talented in explaining

I have never seen any explanation about markov chain so clear!

Thanks a lot. Very intuitive! Taking the time to clarify steps you initially had trouble on makes it much easier for me to understand the thought process behind the concept.

ritvik you are one of the wonders of the world

A very underrated video. I loved the explanation of what 🥧*A=🥧 truly means. This was the first video I've seen it explained. A lot of other videos show the computation (which is trivial) and avoid the meaning behind it.

I'm an undergrad doing stats and learning Markov chain right now. I really like the intuitive explanation of the stationary distribution can be a probability distribution, rather than a fixed state at the beginning of the video. That idea has confused me for 3 months! Really appreciate your video :D

One of the best lectures on Markov chains

It finally clicked! I was very confused about multiplying a distribution by the transition matrix exactly as you were. But not anymore thanks to you. So, a HUGE thx!!

Glad you clarified that point. I definitely had the wrong notion that stationarity meant eventually getting there.

That subtle point is something that I had the misconception of before. Thanks for pointing it out and clarifying.

I really enjoy that you explain these concepts so simply! Thanks!

I use the eigenvector trick to find stationary distributions regularly for marketing applications, such as finding the steady state distribution of market shares

The best video so far I’ve watched regarding the topic! Thank you! And keep updating more content pls

Great Explanation. Looking forward for the coding. And the rest of the playlist. And as always, thanks.

your way of explaining things is amazing. I've actually been studying some of the way you struture your explanations to improve my videos

Your videos are quite good at explaining machine learning concepts with all the maths in the background. Thanks a lot!

love your explanations! really cleared up some of my confusions. please make more videos like this!

This is so so well explained omg

Really appreciate how you keep bringing it back to intuition but also do the math. well done dude!

My visualization of the chain being "at a distribution" is to imagine, say, 1000 particles in the system. Each one moves around according to the transition probabilities. A "steady state" is where the particles leaving a node are exactly replaced by new particles entering the node.

Awesome explanation! Thanks for all the posts ritvik😊

Love your videos, thanks for making them. I just wonder who gave a dislike to this amazing video.

definitely downloading this one .. Nope, I don't trust youtube to keep this gem alive for too long.

Very clear and intuitive, I definitely learned something.

great video! good mix between intuition and math

It took me 9 videos to learn that you have an intro pen flip

This is such an awesome channel!

I was kinda hoping for you to explain what would hypothetically happen in your magically corrected scenario, although I believe it would not be possible. Great work and great material!

You and Josh Starmer really should get together and do some content together! I have learned more from the two of you than I learned in college!

Another great video 👍 You have a good teaching style, clearly born from others poor styles you have experienced 😂

Thanks a lot for your awesome explanation! ❤

Great explanation indeed! Thanks bro.

This is excellent, thank you very much!

You are such an awesome teacher. Thanks for this video =)

super video. request you to add more on other properties (reducibility, reversibility, time homogeniety, periodicity, ergodicity , mixing times & why these are necessary)

Loved it!

This is explained amazingly, thank you!

Bhai, you are just awesome!

Very good job man. Thank you a lot!!

MAN THANK YOU SO MUCH!. This will save my ass

Very clear explanation

saved my day ! thank u

Good job 👍... learnt some vital concepts

THANK YOU

Hey, would you consider this topic advanced and if yes, what textbook would you recommend for learning advanced topics?

masterpiece! you made me feel that education is kind of art! :D

Great explanation as always! One question I have is, since this is a stationary state, shouldn't we consider both the channels going out and the channels coming in? Such as for B, shouldn't we write an equation like: pi_C * 0.5 + pi_E * 0.1 - pi_B * 1 = pi_B ? Why didn't we count the channels going out?

Great video.

amazing so clear

This is amazing content. Keep it going!

Amazing video! Especially the part about how to get the steady state by using the Eigenvector equation was eye opening for me. One question: I am starting out on markov-chains and I would like to know how I can generate the trasition matrix in the first place. Let's say I have a dataset with some timeseries. How do I start clustering the states? Do you have a video on that?

Hello man, thank u so much for this helpful videos. Im a huge fan of you and I want to askin u about a problematiq that i think of it, is about using neural network on forecasting time series and how about the resulats ? Is it better than the ARIMA models ??

thanks!

Nice video. Here it seems that the example in the video does not meet the Detailed balance equation (e.g., P(B)*T(C|B) != P(C)*T(B|C)). Is it safe to say that Detailed balance is sufficient but unnecessary for Stationary Distribution?

That‘s amazing!!! Thanks!

So the steady state is like the probability of being at a certain node as time t goes to infinity? Like a limit??

Nice video, i have two questions 1. How did we come about 1/5, 2/5 and 2/5, i know of the first and last zero. 2. In a case where we are told to calculate the probability of ending in the third state after 4steps, if we start from state 1..how do i do this?

Nice. But I still like it when you explain with a real world example.

Sick shirt!

Question: suppose we wanted to calculate P(A) for some reason. It's certainly not the case that P(A)=0, since we of course could start at state A. So how would we go about calculating this? I know that normally we compute stationary values, but in this case it would be 0. Looking forward to hearing responses.

Like two conditional steady states with the last example.

It is understood that the probability of being in state A is zero in the next time step but how come intuitively the probability of being in state E can be zero? After all the probability of self-transition is 0.9!

Can you provide a real world example where we have finite discrete number of states such that this would be useful?

Ritvikmath : Lingayat Jangam?

I'll be damned if this isn't a good explanation