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.

@ritvikmath

3 жыл бұрын

Wow thanks for the kind words!

@danielwiczew

3 жыл бұрын

@@ritvikmath Hope you will boom at some point. Generally a canal's popularity curve is sigmoid and there's inflection point at some point.

@zhixiangwang7165

2 жыл бұрын

@@ritvikmath really great lecture!! better than all the courses I have learned

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

@ritvikmath

2 жыл бұрын

Hey thanks!

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!

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

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.

One of the best lectures on Markov chains

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

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

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.

@ritvikmath

3 жыл бұрын

Glad it was helpful!

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!!

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

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

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

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

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!

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

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

@ritvikmath

3 жыл бұрын

More to come!

This is such an awesome channel!

Awesome explanation! Thanks for all the posts ritvik😊

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

@ritvikmath

3 жыл бұрын

Thank you! Will do!

This is so so well explained omg

great video! good mix between intuition and math

This is explained amazingly, thank you!

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

@ritvikmath

3 жыл бұрын

Awesome, thank you!

Thanks a lot for your awesome explanation! ❤

Very clear and intuitive, I definitely learned something.

@ritvikmath

3 жыл бұрын

Great to hear!

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.

Great explanation indeed! Thanks bro.

Good job 👍... learnt some vital concepts

Very good job man. Thank you a lot!!

This is amazing content. Keep it going!

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

This is excellent, thank you very much!

@ritvikmath

Жыл бұрын

Thanks 🙏

Bhai, you are just awesome!

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

@ritvikmath

3 жыл бұрын

Glad it was helpful!

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

amazing so clear

saved my day ! thank u

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

@ritvikmath

2 жыл бұрын

Super cool application!

@joelrubinson9973

2 жыл бұрын

@@ritvikmath I also use it for digital journey analysis to project add to cart events as a state with other states such as branded search, generic search, competitive brand search, viewing product pages inside of amazon for your brand, competitors, etc. I use add to cart ad the conversion metric because purchase would be an absorbing state. works great!

Loved it!

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

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

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!

@ritvikmath

3 жыл бұрын

Indeed, before the fix we had a defective Markov Chain, good eye!

That‘s amazing!!! Thanks!

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?

Very clear explanation

@ritvikmath

3 жыл бұрын

Glad you think so!

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

@ritvikmath

3 жыл бұрын

Haha, no shame. It is quite subtle.

@gigz54

3 жыл бұрын

There is also a *snap* *point* "see ya next time" sign off

Great video.

@ritvikmath

Жыл бұрын

Glad you enjoyed it

THANK YOU

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

@ritvikmath

3 жыл бұрын

Great suggestion!

thanks!

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?

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 ??

Sick shirt!

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

@ritvikmath

3 жыл бұрын

I wouldn't consider this an "advanced" topic in the context of Markov Chains. Anytime you talk about Markov Chains, the question of steady state is a natural one.

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?

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. 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?

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.

@adamtheanalyst

2 жыл бұрын

Or is it not possible to calculate P(A) unless the Markov Chain is an irreducible recurrent chain?

Like two conditional steady states with the last example.

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

@ritvikmath

3 жыл бұрын

Thanks! And noted :)

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

@pedromanuelmartinmesa6098

3 жыл бұрын

in the first video about markov he has one of weather (sunny vs cloudy). Simpler but 'similar to real world case' kzread.info/dash/bejne/oqaOr9KNmMW7Y6g.html

Ritvikmath : Lingayat Jangam?

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!

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