Markov Chains: Simulation in Python | Stationary Distribution Computation | Part - 7
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- Опубликовано: 3 июл 2024
- So far we have a fair knowledge of Markov Chains. But how to implement this? Here, I've coded a Markov Chain from scratch and I've mentioned 3 different ways of computing the stationary distribution!
#markovchain #datascience #python
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Absolutely great, useful and easy to follow. Many thanks!
This is an absolutely great channel. i was reading page rank in the university and thought i understood markhov chains well, but upon watching your video i realised there was so much more intuitive things to learn, Thanks a lot!!!
Great Video! Thank you
Excellent! The eigenvector perspective is cool - shows how they, once again, reveal the most important characteristics of a system.
Yeah...exactly!
Man your outro is awesome!
Lol just solved this problem for my code right before you posted this video! Your explanation would have saved me a decent amount of time
Haha...it happens XD
Great video! Greetings from Argentina
Thanks mate! :D
Great video!
I have a doubt. I think the probability function should be divided by "len (seq)", do I?
How could I plot a graph of the random walk over each iteration?
Just a question. If I would like to find outlier sequences, say, the ones with a very low probability, it is clear that I just need to use your last example and calculate some kind of percentile threshold on my dataset of probabilities of chains to isolate the outliers. I am dealing with sequences of different length, so, bigger sequences will naturally tend to 0 due to the fact that probabilities are between 0 and 1. The question is... I am using geometric mean of the transitions, this is, after multiplying all the probabilities like you do, I calculate the nth root of that product, this to get a "better value" that is not affected by the chain length (in this case n where n can be between "a" and b" for some a,b) in order to be able to compare them, is this the correct approach for this problem ? Thanks
U r the best
Keep supporting :D
can you make video on HMM practical implementation please
Supose that we a huge A matrix (realy big dimension). Would be faster/cheaper to use the nontecarlo aproach rather than finding the eigenvectors?.
Thank you for your Video, I just want to know some more details of the last part. In how many iterations of the Markov chain do you expect that see the specific random walk? As we know by increasing the iteration of random walks it would be more chance to appear spesific random walk. can you please clear this part?
To quote South Park:
"Pizza, french fries, pizza, french fries....see? he should of done pizza instead of french fries."
How do you determine the probability of the transition matrix?
can you make practical implementation video on HMM please
Method 2 curls my nails a bit. Instead of of multiplying A many times you could just square A again and again, which will lead to A^(2^n) after n steps. That would be so much faster.
Good point, but at this level of matrix dimensionality it computes fast enough anyway...
can you make a full course on stats and coding? i would be wiling to pay for the service. thanks for the content.
I don't know if you mentioned, but google's search algorithm uses these stationary distributions!!
Yeahh!...No, I didn't mention that in this video.
is it possible to trade NIFTY india using MARKOV chain ? mostly on the LONG side?
Is there a way of verifying the values of stationary state?
He's just shown you three methods. If they agree, no problem. In an earlier vid if the system is oscilatory the stationary state will not settle.
I can has Baum-Welch algo next pls?
Hi. How to do it for time series data?
Very good point, especially where the states have long lives or variation time between or in transition
How to run this
Hi. Awesome videos! Which college are you/were you from?
Kalyani Government Engineering College.
Why didn't you use FOR loops instead of WHILE loops?
Haha...no specific reason
Last time it was a hamburger and not a hot dog😂
Really? 😂😂