Markov Chains : Data Science Basics

The basics of Markov Chains, one of my ALL TIME FAVORITE objects in data science.

[diegososa]

Brilliant explanation, I cannot thank you enough. Markov chains are so important, we easily get lost in linear thinking, Markov helps us see probabilities differently. More videos on this topic would be highly appreciated.

[RD-zqky]

Yes, I would like more videos on Markov Chains. Thank you for your videos.

[ingenierocivilizado]

Incredibly useful! You achieve to explain difficult concepts in a straightforward and easy way. Thank you for these videos!!

[beyerch]

How exactly is Sunny W2 0.44? If there is a 0.3 chance of the day after a sunny day also being sunny day how did your probability INCREASE for W2? Seems there is either an error here or something was left out of this explanation?

[stephanieb.]

Sir, I think I am in love with you. How is it possible that you explain everything so simply and clearly, and my teacher sucks at making me understand what a Markov chain is???? Why aren't university teachers taught how to teach and explain the material in such simplicity???? Thank you for your explanations. You have helped me through linear algebra. THANK You were born to teach.

[jakecamposano]

this is the first video that I understand the Markov chain thanks you i watch the commercial to pay you thanks a lot

[ramankutty]

You have a wonderful knack for explaining concepts. Thank you

[CleverSmart]

This is so brilliantly well explained, thank you. I was not getting it at all before.

[nikkatalnikov]

A brilliant into, thank you!

Just a small addition: steady-state vector is (unsurprisingly) an eigenvector of transformation matrix with the corresponding eigenvalue (once again,  unsurprisingly, as the matrix is normalized) of 1.

A video on Monte Carlo Markov Chains would be nice.

[anaibrahim]

that example you gave at the end i was looking and asking people one by one but null gave me a direct simple answer like what you gave us in that vid thanks a lot for the vid
deserve the subs and likes
keep up the good work

[minrongwang]

I love your tutorial. It is very helpful. Thank you.

[DanielLopez-mkih]

Good video. I did not get how do you get the 0.44 and 0.56. And i suppose that the 0.30 was only an assumption right? Thanks for the video, and if you can explain me that it would be awesome

[sumitkumarpal]

Just a comment: If we calculate using {S(t)} = [Transition Matrix] {S(t-1)} for W2 then we have to transpose the matrix shown at 3:33. Please correct me if I am wrong.

[thegoodgorilla]

Thank you sir! I finally understood the Markov Chain concept now!!

[xinyuan]

Thanks so much as always for the great video 🫰It also feels very philosophical to convince someone to not dwell on the past or anxious about the future, “The future is independent of the past given the present.”

[eduardocruces]

Nice job! clear and simple explanation.

[kanzzon]

Have you done a contrast diagram with past data to find out the acuracy? If so, do you have a video showing it?

[ledz]

Thank you for the clear explanation! Have a video request for Conditional Random Fields

[wendycastillo]

Hello how did you calculate W2? Thank you!

[NicolaevM]

Great explanation!! It would be great if you could overview DSGE models (often used in econometrics), they also have steady state.