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.
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.
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 :(
Same here :)
Your simplistic explanation is rare and very interesting!!!
I think you should post more videos!!
This is very helpful. I've come back to this video several times as a refresher.
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.
Explained in the simplest way possible. Would love to see Conditional Random Field algorithm as well.
The best so far for an introduction.
This is the best explanation so far.
Take it from Rommie: Markov Models are great! - Wow! Great introductory video and made especially for me ;)
I couldnt hear what you said over the sound effects
Thanks so much for such a simple and clear explanation!!!
fantastic ;-) please do some more videos for presenting such concepts, beautifull
Thank you so much for this! Your explanation was super clear and to the point!
In the video mistakenly 1.0 has been added in the last column of first row of the transition matrix.
Is there a specific steps in markov model? Pls answerrr
that was an awesome explanation! please do make more videos like this!
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.
Very well done, thank you for a clear explanation!
Great thank you. We use Markov by increment position in radar detection.
Great work man. Simple and superb explanation.
Great explanation, in fact we also use Markov to create movement dr/dt in radar technology.
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.
why is this video so underrated man
The presentation is brilliant.
I like this video very much...thanku it help in my project very much
Marvelous explanation!! Thank you!
great videoreally awesome! thanks
Neat animations and clear explanations!
simplest explanation ever
Best video on markov models!
Great introduction!
Amazing video,
best explaination till now..
Someone tell me practical applications for this?
Good - love the graphics!
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.
Nice explanation, thank you!
Really helpful! Thank you! Thank you! Thank you!
Do more videos on this topic...
Good explanation..
Markovelous explanation
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 .
Best video on this topic fs
You Should Be Thanked More Often.
Great video
nicely done
Markov models are great
nice work!
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 :)
Thank you!
Well explained
Please sir if there possible to uplode me the slides
Very clear.
Thanks! It really helped!
You're amazing!
love it ! thank you
Love from Pakistan 🇵🇰❤️
Already tossed my textbook into the trash can.
Thanks
Awesome!
while the animation was great, i think you over-did it with the sounds effects. especially the "punch" sound is super annoying :(
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 :)
thank you this help alot ....don't forget to like and subscribe yall
That's awesome
Muito bom
Wow !!
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 !