Markov Models
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- Опубликовано: 6 окт 2024
- Markov models are a useful scientific and mathematical tools. Although the theoretical basis and applications of Markov models are rich and deep, this video attempts to demonstrate the concept in a simple and accessible way by using a cartoon.
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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.
Sad story :(
This is very helpful. I've come back to this video several times as a refresher.
Your simplistic explanation is rare and very interesting!!!
I think you should post more videos!!
Explained in the simplest way possible. Would love to see Conditional Random Field algorithm as well.
Hands down one of the best introductory videos on this topic!
You taught a lot in three minutes and I learned a lot in three minutes. Thank you.
The best so far for an introduction.
This is the best explanation so far.
Thank you so much for this! Your explanation was super clear and to the point!
Take it from Rommie: Markov Models are great! - Wow! Great introductory video and made especially for me ;)
Thanks so much for such a simple and clear explanation!!!
Very well done, thank you for a clear explanation!
I like this video very much...thanku it help in my project very much
fantastic ;-) please do some more videos for presenting such concepts, beautifull
Great thank you. We use Markov by increment position in radar detection.
that was an awesome explanation! please do make more videos like this!
Marvelous explanation!! Thank you!
Best video on markov models!
The presentation is brilliant.
Good - love the graphics!
Great explanation, in fact we also use Markov to create movement dr/dt in radar technology.
why is this video so underrated man
Really helpful! Thank you! Thank you! Thank you!
Neat animations and clear explanations!
Amazing video,
great videoreally awesome! thanks
Nice explanation, thank you!
Great introduction!
In the video mistakenly 1.0 has been added in the last column of first row of the transition matrix.
Great work man. Simple and superb explanation.
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.
Markovelous explanation
love it ! thank you
Thank you !
best explaination till now..
Best video on this topic fs
Very clear.
I couldnt hear what you said over the sound effects
simplest explanation ever
Thanks! It really helped!
Is there a specific steps in markov model? Pls answerrr
Do more videos on this topic...
nice work!
Markov models are great
Nice video. Do you reach a steady state after multiplying the Markov matrix many times?
Yes, in this case you do. Though there are some transition matrices that will not converge when raised to a large power.
You Should Be Thanked More Often.
Good explanation..
Great video
nicely done
Awesome!
Well explained
You're amazing!
Hi ! Nice explanation. Can you tell how to calculate the probability (1.18sec.) 0.5, 0.4,0.1? please
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.
@@lanevotapka4012 Hey Lane, thank you so much for your answer.
That's awesome
Someone tell me practical applications for this?
Rommie probably doesn't like the fact that you are trying to predict where she will be and can do so into infinity lmao
She saw this, she doesn't mind :)
Love from Pakistan 🇵🇰❤️
The sound effect is a bit too loud.
what is starting probabality factor??
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.
Good question Vijay .
Already tossed my textbook into the trash can.
thank you this help alot ....don't forget to like and subscribe yall
Muito bom
Wow !!
Please sir if there possible to uplode me the slides
Did anybody else try and work out the chances of where she will be in the next few periods and accidentally fry your brain?
That's why you just let the computer do it for you :)
while the animation was great, i think you over-did it with the sounds effects. especially the "punch" sound is super annoying :(
the lined paper is annoying, it is not required
Markov models are a pain in the ass…ooops
yay, not a video of a homosapien drawing illegible hen scratchings on a white board
Thank you!