Markov Matrices 

Huge thanks to you!! Very clearly explained at a comfort pace. Its nearly final and my teacher's only covering the theorems and some calculation examples. This mit series really showed me what matrices could achieve and the connection between concepts. (I especially like the fibonacci part and this partical part) Good job!

Herein we observe an advantage of being left-handed. :)

Such brief and impeccable lecture Totally enjoying it

Thank you! Good explanation. But I think it is not necessary to calculate the decomposition A = UDU-1. We know that the probability after k steps is Pk = c(λ1)(^k)x1 + d(λ2)(^k)x2 where x1 and x2 are the eigenvectors and λ1, λ2 the eigenvalues, with P0 we can calculate the coefficients c and d for k=0. After 100 steps the probability is Pk for k = 100.

love his enthusiasm :) Good video

Best one yet. Really cleared everything up in this chapter.

This guy can explain things well. He says, "Welcome back." Now I’m trying to find the first video for which this video is a sequel. Could someone tell me where that first video is?

The Markov matrix A is a transpose of what is usually presented.

So I sort of understand right until the end. With the final probability for n = infinity, being one third, one in two; how does that translate to the answer to the question 'What is the probability it is at A and B after an infinite number of steps'. Is the answer that it's six more times as likely to be at B than A ?

So interesting lecture and problem on Markov matrix!

Wow quite amazing problem❤❤❤

Very good video and very clearly explained.

Very well explained !!

Very well explained, thank you

Rad, thanks!

Very helpful video. Thanks mit

Love this

This guy reminds me of Will from good Will hunting

this was great

I get different eigenvalues: (1, -0.2)

Goat

Thank you, m7

ty fam

cool

gg

For an MIT solution, it lacks some proof. We do not always see, we need a detailed explanation. But it is fine.