Markov Models

[@achmadaqsahusain5046]

I have learned more about markov chain from your three minute video than my professor the entire semester. Thank you. Keep up the amazing work.

[@denizgunes7262]

Sad story :(

[@dhirgajbhiye06]

Your simplistic explanation is rare and very interesting!!! I think you should post more videos!!

[@MyVinodkumar]

Explained in the simplest way possible. Would love to see Conditional Random Field algorithm as well.

[@melissaedmondson5956]

This is very helpful. I've come back to this video several times as a refresher.

[@n00b_ninja]

Hands down one of the best introductory videos on this topic!

[@stephennalewanyj6904]

You taught a lot in three minutes and I learned a lot in three minutes. Thank you.

[@masterpee2328]

The best so far for an introduction.

[@leonhardeuler9839]

This is the best explanation so far.

[@rominawild6235]

Take it from Rommie: Markov Models are great! - Wow! Great introductory video and made especially for me ;)

[@anonymous.reviewer]

Thanks so much for such a simple and clear explanation!!!

[@yashkaliapiano]

It was a good explaination but I would suggest using sound effects a bit more sporadically as there's a lot more sound in there than it needs to be.

[@vinaybhujbal4347]

why is this video so underrated man

[@shusrutorishik8159]

that was an awesome explanation! please do make more videos like this!

[@rykhan2003]

fantastic ;-) please do some more videos for presenting such concepts, beautifull

[@ericgamor9975]

Great work man. Simple and superb explanation.

[@anupamasoni6891]

I like this video very much...thanku it help in my project very much

[@hgtrad7655]

Great thank you. We use Markov by increment position in radar detection.

[@ananalysisofthingsinlife.8561]

Thank you so much for this! Your explanation was super clear and to the point!

[@nigelptv]

Very well done, thank you for a clear explanation!

[@PaweKaczynski]

Best video on markov models!

[@hgtrad7655]

Great explanation, in fact we also use Markov to create movement dr/dt in radar technology.

[@dustin_echoes]

The presentation is brilliant.

[@otiebrown9999]

Good - love the graphics!

[@prithvirohira1372]

simplest explanation ever

[@DeepCrazyWorld]

Amazing video,

[@vinaybhujbal4347]

In the video mistakenly 1.0 has been added in the last column of first row of the transition matrix.

[@benjaminjordan2330]

I couldnt hear what you said over the sound effects

[@bcbcbusy]

Neat animations and clear explanations!

[@inazuma3gou]

Nice video. Do you reach a steady state after multiplying the Markov matrix many times?

[@lanevotapka4012]

Yes, in this case you do. Though there are some transition matrices that will not converge when raised to a large power.

[@cyunmingsiu3555]

Marvelous explanation!! Thank you!

[@amreshjha1357]

Great introduction!

[@blabbery5698]

Is there a specific steps in markov model? Pls answerrr

[@nithyagogtay3145]

Really helpful! Thank you! Thank you! Thank you!

[@AlexVoxel]

Nice explanation, thank you!

[@kimiyamaleki8368]

great videoreally awesome! thanks

[@Hamza-vw4rz]

Markovelous explanation

[@bhavya2301]

You Should Be Thanked More Often.

[@RushikeshTade]

best explaination till now..

[@mathankumars896]

Hi ! Nice explanation. Can you tell how to calculate the probability (1.18sec.) 0.5, 0.4,0.1? please

[@lanevotapka4012]

Glad you found the video helpful. Those are transition probabilities, and one way to obtain them is to observe the system (Rommie) for a long period of time as she stochastically transitions from a given state (her house) to the other states. Let's say, for example, you observed 20 transitions, and she went to work 10 out of 20 times, you get a 0.5 probability. If she went to her house 8 out of 20 times, you get a 0.4 probability, etc.

[@mathankumars896]

@@lanevotapka4012 Hey Lane, thank you so much for your answer.

[@WhatAdityaDoes]

Best video on this topic fs

[@divyahatti9765]

Do more videos on this topic...

[@AhamedKabeer-wn1jb]

Good explanation..

[@AbhishekVerma-kj9hd]

Markov models are great

[@ancachiriac3838]

Thank you !

[@Diego0wnz]

Someone tell me practical applications for this?

[@trishaneelgogoi4980]

Great video

[@hiteshkumar3076]

nicely done

[@wazxserd]

nice work!

[@thelabadabada]

love it ! thank you

[@FlywithZahanat]

Well explained

[@danilzubarev2952]

Very clear.

[@pickarpit]

Thanks! It really helped!

[@stephenogbodo1328]

You're amazing!

[@facebren]

Rommie probably doesn't like the fact that you are trying to predict where she will be and can do so into infinity lmao

[@lanevotapka4012]

She saw this, she doesn't mind :)

[@bimbrawkeshav]

Awesome!

[@derejehailemariam677]

The sound effect is a bit too loud.

[@sanjaisrao484]

Thanks

[@yulinsun8873]

Already tossed my textbook into the trash can.

[@vijayramadurai1495]

what is starting probabality factor??

[@lanevotapka4012]

The starting probability vector is something that you need to construct yourself or is provided to you. Each place in the vector represents the state, and you need to put the probability that the system starts in those states. That means, if we know that Rommie (in the movie) always starts from home, then we put a one in that spot of the starting probability vector, and put zeros everywhere else.

[@viveksapate3272]

Good question Vijay .

[@nourabd8261]

Please sir if there possible to uplode me the slides

[@yourcoffeee]

That's awesome

[@haziqkhan3377]

Love from Pakistan 🇵🇰❤️

[@professordeano]

thank you this help alot ....don't forget to like and subscribe yall

[@swayamshreepatra4887]

Wow !!

[@lucaslopesf]

Muito bom

[@evetrescoemes5522]

Did anybody else try and work out the chances of where she will be in the next few periods and accidentally fry your brain?

[@lanevotapka4012]

That's why you just let the computer do it for you :)

[@homeXstone]

while the animation was great, i think you over-did it with the sounds effects. especially the "punch" sound is super annoying :(

[@meocats]

the lined paper is annoying, it is not required

[@715Clipss]

Markov models are a pain in the ass…ooops

[@anglewyrm3849]

yay, not a video of a homosapien drawing illegible hen scratchings on a white board

[@hazemwhite6561]

Thank you!