Markov Chains & Transition Matrices

[@nehabose3181]

Best video on Markov Chains. So easy to understand and no unnecessary analogies. Great job!

[@blacksages]

THANK YOU! God, i finally understood how to make the damn matrix. My professor is so bad at explaining things and he just goes by the sum version, and doesn't explain things. This is such a life savior !

[@LisaZuckberg]

OMG! This is the best video on Markov Chains. I just spent 30 mins on reading articles on medium, brilliant, wikipedia, etc and couldn't understand what they meant at all. But 4 mins into this video, I got it!

[@tyfoodsforthought]

So crisp. So clean. So clear.

[@DrTrefor]

Thank you!

[@dhrumilburad5859]

What a video man, what an explanation. I literally understood the concept in one go. Keep it up !!!!!!

[@titobruni987]

I've been watching math videos for a few years and I have to say that your channel is the best. You just teach in a extremely organized and interesting way. Please keep on!

[@DrTrefor]

Thank you so much!

[@kidnard6017]

Dude, you are a magician, the way u explain it ! Seems so easy, and make so much sense, thank you so much ! Please do more part !

[@jimf2482]

Dr Trefor, you're a blessing. Thank you for such clear explanations. They're liquid gold.

[@frozenkingfrozenking6989]

Good, very good. Some people on ytube are just afraid of writing math when ever they are teaching, and just mistify the subject. This is good math indeed.

[@anaibrahim4361]

don't know how to thank you sir this deserves to be paied for really great job and pure gold

[@steng9887]

Simply excellent explanation. In 6 minutes you made me understood what I tried to study in a week

[@SAAARC]

The explanations are easy to understand and the video length is at the sweet spot. Great job! Looking forward to the rest of the series.

[@DrTrefor]

Thank you, glad you're enjoying!

[@Anthony-ig6ds]

BEST EXPLAINATION EVER I"M COMING BACK TO SEE IF YOU CAN EXPLAIN THE HARDER STUFF THIS WELL

[@amankrishna751]

Give this man an award!

[@databridgeconsultants9163]

Wow. Thank you very much. What a way to make this look so easy. I understood this concept for the first time in my life.

[@joelipowski8393]

Crystal clear explanation. Direct and easy to understand. Thank You!

[@JohnSmith-qp4bt]

Clear explanation. Well poised and articulated. Makes its interesting, even without illustrating a real life practical example in the video. Also, a true desire to teach.

[@gouthamshiny3346]

best explanation I could find on youtube

[@DrTrefor]

Thank you!

[@oleksandrrechynskyi7636]

that's what I call a straightforward explanation. Thank's a lot!

[@xxg-forcexx8734]

Generally speaking the rows are "from state A" and the columns are "to state B" within the literature (so invert his matrix along the diagonal) and it would have been nice to see the even simpler form of P using eigenvalues and eigenvectors to create AD(A^-1)=P to even better show how this generalises transitions and then shows the rate at which the markov chain converges

[@sjchxhtriskit3025]

Was wondering this lol

[@ww905]

Wow, I was writing up my thesis on TMMC application to my little chemical adsorption model and I cannot understand the Maths behind it properly. You saved my life.

[@tongtong168]

Your explanation is much better than the Khan's Academy lets say. So detailed and so simple to understand.

[@DrTrefor]

Thank you so much!

[@anti-tankartur677]

His video is completely wrong about the matrix positioning

[@brandoncazares8452]

Thanks for this, I finally understood it more than my professor because he didn't explain this at all.

[@skepticbubble3166]

Our Markovian hero, thanx

[@mohammedalsubaie3512]

thank you for your video it is well explained, but at 3:19, the matrix isn't supposed to be the way around? I mean the 0.25 shouldn't be in the place of 0.4? because the rows explain the directions, not the columns?

[@MrVoronoi]

yes, you are right

[@justsayin...1158]

Thank you for this very practical video, I was immediately able to apply this concept, although I didn't immediately understand why multiplying the transition matrix with the current state vector yields the next state vector, but after some further consideration, what this multiplication actually does, it is quite clear, why/how that works.

[@Sid-xt3kt]

This guy saving my linear algebra grades

[@Sid-xt3kt]

also i just realized that markov chains look like finite state machines

[@gautam1940]

Lovely. I think even Markov would not be able to explain like that !!! Liked and Subscribed!!!

[@DrTrefor]

Thanks for the sub!

[@thomasmale2302]

I liked your explanation it was simple and clear, thank you so much.

[@shreyasingale]

Thanks for the lucid explanation!

[@laxshanganasan1680]

i have an exam today on this topic and you clearly explained it to me

[@pallabkumar5775]

That was a wonderful explanation of the Markov chain, thank you

[@leandrocabezas7379]

I am impressed, wayyyy too good. Liked and Subscribed

[@elakhe-llonamlomzale4774]

Simple and comprehensive, thank you

[@tristanlouthrobins]

Incredibly good explanation of Markov Chains. Subscribed!

[@DrTrefor]

Welcome aboard!

[@arsenalaman6493]

Amazing video sirrr......Thank you for video. Loves from India

[@omedhassan2190]

Simple and comprehensive.Thank you soooooo much

[@MrMahankumar]

I cried. This was very good

[@taotaotan5671]

So we can apply eigendecomposition to simplify the matrix exponentiation! Thanks Trefor!

[@DrTrefor]

Absolutely! That was beyond the scope of this video, but would definitely be the next thing to do.

[@interest21stcentury74]

Wow! Interesting Topic! Thank You for covering something wonderful!

[@DrTrefor]

Glad you enjoyed it!

[@hnrajaonarison5034]

I really liked your easy explanation. Thank you.

[@arjunag7553]

You, Sir, are a Superhero.❤

[@kazeemkz]

Spot on delivery Dr, many thanks

[@HM-he1ob]

You had shed lights to people like me who suffered a lot from a college class which takes about 90 min

[@mr2seis388]

This guy gave a 6-minute crash course where I started so confused. my man.

[@PardeshDhakal-x4b]

Very well explained sir! Thank you.

[@safwanrushdan5260]

i am safwan, good video👍🏻🙏🏻

[@kekoHere0610]

You just saved me ! Thanks

[@devendraparmar7068]

Beautiful video Sir..👌👌

[@korakatk318]

Awesome video!

[@praisesharon4241]

Very nice explanation

[@蔡小宣-l8e]

Thank you Dr. Trefor Bazett! 谢谢！

[@noahelekhtra9456]

I was tasked to program it with c++ , loving the challenge so far 😂😜

[@anya7us]

Lovely explanation

[@MuhammadAli-ut1sh]

Awesome , cleared my concept , Thank you !

[@Darkev77]

Brilliant to say the least

[@ayyoubm]

GREAT EXPLANATION!

[@OfferoC]

very good explanation. thank you.

[@surgeonrecords]

very clear. nice work.

[@luisanaencarnacion6050]

Thank you man! This was so helpful☺️

[@BrotherNineinChicago]

Brilliant explanation thank you :)

[@dewanmohammedabdulahad527]

Thank you for the lecture. It's easy to understand. Do you have any plan on Non-linear control theory (obeviously in easy way llke you taught now)?.

[@hiamy1250]

omg this video helps me a lot! thanks a ton

[@anderson4429]

absolutely amazing

[@joejoe-lb6bw]

Nice! Even I understood that.

[@itays7774]

Also, the diagonalization of a general two state transition matrix is quite nice, so taking a high power of one is not so bad

[@AryanKumar-qo6fi]

Respect!!!!!!✌✌ >>>Legend👏

[@lucycai3356]

at 3:32, I think the row in the matrix should add up to 1. am I correct? Thanks!

[@williamolenchenko5772]

Columns add up to 1.0. Not the rows.

[@michaelc.4321]

This just blew my mind because it made me realize that the final convergent state of a markov chain is dictated by the transition matrix's eigenvector corresponding to its largest eigenvalue because the repeated multiplication essentially comprises the power method of finding the largest eigenvector/value.

[@teflonpan115]

Thank you for confusing me. Great work 👍

[@slamburglar909]

great video! there's so much more you can talk about concerning markov chains, this is just the beginning! Like how they can limit to some stationary matrix under certain conditions of the transition matrix P, or even easier ways to calculate P^n (if you decompose it such that P=U D U^-1, where U is the matrix of eigenvectors and D is the matrix of eigenvalues, then P^n = U D^n U^-1, where D is simply the matrix of only eigenvalues^n along it's diagonal). They are very interesting indeed, you have your work laid out for you! XD

[@DrTrefor]

Totally! I am thinking of doing some follows we are just scratching the surface here

[@marcocaballero]

Great video, thanks!! Any chance to follow up on this topic? Perhaps look into Markov Models?

[@eliasdargham]

Absolutely clear and concise, thank you! It worth noting however, that computing the P^n matrix is very computationally expensive, is there a better way to to solve for P^n without having to do the power?

[@aiswaryavijayan260]

Saved me👏

[@SiriusFuenmayor]

Great! very clear and concise, what is the connection of this with turing machines?

[@Darkev77]

If we were to line up the probability distributions to = 1 along the rows, rather than the columns that wouldn’t work (keeping the vector unchanged). Is that because of how it’s defined, due to the notation used?

[@DrTrefor]

Indeed, it's just a quirk of the definition. If you wanted to do it your way, you'd have to be multiplying with the vector on the left instead, which would be just as good but not as conventional.

[@internationaleconomics2327]

Thank you it was usefull

[@freedmoresidume]

Incredible 🔥

[@lume-eugene.h2161]

Thank you, I think I will be able to ace the CS 70 final exam at Berkeley.

[@ThabetMarwa]

This was absolutely brilliant. This video could also be used to explain quantum spin 1/2; just make a and b stand for spin up and spin down

[@TheVarun6]

Very neat👍

[@gloriashen2671]

Best explanation ever!~!!

[@DrTrefor]

Thank you!!

[@somenewkid6892]

wow it just so happens to be that the lecture today included transition matrices! what luck!

[@DrTrefor]

nice timing!

[@BlackCodeMath]

Beautiful.

[@murthyrallabandi]

It sounds good, i can apply this to Roulette game! 😅

[@vwlh8r]

I did not see a link to the video you referenced introducing matrix multiplication

[@DJ-dk3hh]

I am a bit confused on how we came up with S0, if we had 3 vectors how do you come up with S0? Watching the previous video helped me understand how S1 was derived, but cannot understand how S0 the initial state was derived. Why not .5/.5?

[@KhoaLe-oc6xl]

Your 6 minutes = my professor’s 1 hour

[@johnwick-fw7ey]

thanks sir

[@somcana]

Why would some one dislike your Videos. They must be in a dislike Markov state. I wonder when they will transition Dr Trefor Bazett.

[@currrrrrled]

Fantastic!!

[@arsenalaman6493]

I am Almina khatun who also comment on our video sir.......I al ways first

[@anaisliu6709]

This is an awsome video however I am still confused that is it possible to calculate the transition matrix using only the initial probabilities? Or calculate the initial probabilities using only the transition matrix?

[@MyFirstReurveBow]

thank you for your videos . if you will explain the logic behind it and not the matrix structure / equation structure perspective it will be much easier to understand. also first video is not on the list

[@j.k.sharma3669]

Very nice

[@TheDancingMudkip]

5:11 Yeah how did you get that?

[@highinstitute2366]

Thanks❤

[@anishjoshi1999]

love from nepal

[@kianushmaleki]

Does this non-Markovian system turns into a Markovian system if we let n -> Infinity ?

[@jjlarochelle2523]

How do you find at what value n the S vector will have a given value for x1??