Intro to Markov Chains & Transition Diagrams
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- เผยแพร่เมื่อ 9 มิ.ย. 2024
- Markov Chains or Markov Processes are an extremely powerful tool from probability and statistics. They represent a statistical process that happens over and over again, where we try to predict the future state of a system. A markov process is one where the probability of the future ONLY depends on the present state, and ignores the past entirely. This might seem like a big restriction, but what we gain is a lot of power in our computations. We will see how to come up with transition diagram to describe the probabilities of shifting between different states, and then do an example where we use a tree diagram to compute the probabilities two stages into the future. We finish with an example looking at bull and bear weeks in the stock market.
Coming Soon: The follow up video covers using a Transition Matrix to easily compute probabilities multiple states in the future.
0:00 Markov Example
2:04 Definition
3:02 Non-Markov Example
4:06 Transition Diagram
5:27 Stock Market Example
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The most thorough and clear explanation ever. Can't wait for the next video!
Thank you! Follow up video coming next week:)
A subscription to your channel is the gift that keeps on giving.
Glad you're enjoying!
Please don't go to MIT, Standford at all, be here. We need you.
Very clear explanation with easy examples. Thank you!
Very well explained mate. Videos like these are a great into to a statistical topic and is a great foundation to dive deeper into the math behind it
one of the best explanations about Markov chains on youtube. thank you
Subscribed to this channel after about 30 seconds.... amazing
A very well presented and insightful lesson.
Thank you for the 2 part explanation!
Crystal clear explanation , thank you Dr.
I was searching for "Wiener-Lévy process which is also a Markov process" but luckily I ended up here, a wonderful serendipity! Thanks for the simple and concise explanation Dr. Trefor.
Rare moments.. when you understand in the first go.
incredible video with a super clear explanation!
Love your videos man, keep up the great work. You and Presh Talwalker are the best.
I watched around 5 videos , this explained it the best !! thanks alot
You just made my life better all the way in a small country found in Africa called Kenya. Thank you.
amazing explanation. you have a knack for making these difficult topics understandable
thank you so much for this
Glad it was helpful!
Very well explained.thank you
I really appreciate the clarity of the explanation. I am now a subscriber and a fan! Thank you.
Thanks for the sub!
Never seen such explanation before. Amazing Sir
I'm happy tha I found this channel. thank you!
Thank you for making this.
Awesome explanation, Thank you sir. 👍
This was such a wonderfully clear explanation. Thank you so much!
Glad it was helpful!
@@DrTrefor Not to get too greedy, but can you do one on Hidden Markov models? Thanks a bunch again!
A very good explanation!
Perfect. Dr. Trefor you are the best!
Thank you, glad you enjoyed!
Thank you. This was a very good explanation
A simple explanation of something that could be very complexly understood. Thank you, Dr. Bazett
what a sarcasm.
Fantastic explanation
Thank you soo much this is the best explanation ever❤❤❤
really helped to understand this concept in the first go
So good way of explanation
Awesome explanation!
amazing explanation.
You a natural. Thanks , preparing for Risk Modeling and Survival analysis actuarial exam
Thanks I needed this for my upcoming exams :-)
This was strikingly clear and fresh. Loved it!
Your explanation is super!
Wow! Just beautiful! Love way you explain things!
Thank you so much!
Thank you so much Doctor 😍
absolute delight.. exactly at the time I wanted it for an AI implementation
Love this!!!
oh my for this was so helpful thank you so much
awesome job
Great Sir.... the explanation is ridiculous ❤❤
Best explanation of Markov Chains I've seen. Most videos don't explain how you get the initial probabilities, but from your explanation I understood that they're equality distributed at outset (that is if I understood correctly) and can stabilize as frequency outcomes over iterations . Thank you.
However, one thing that wasn't too clear on was if a Markov chain only depends on the current state of predicting future states, wouldn't a tree that predicts into the near or distant future states not be using the Markov property since there's a whole chain of dependencies?
Wow 🔥🔥🔥
عاشت الايادي استاذ شكرا جزيلا بالتوفيق والنجاح
maan you are awsome THANK YOU!!!
Excellent
Awesome thanks
If only the next video was out so I could make a Markov chain to predict when the next video will be out
Haha, well past behavior indicates I release a lot of videos on Monday’s, but you can’t look at that unless you model with a non-Markov process;)
Thanks!
Thank you dr, yours vidéo are usefull.
Thank you!
THANK YOU
nice explanation for a beginner like me
I really appreciated your video. I have a question: in the market example with a drift rate of zero the transition probabilities would have been even? I mean 50 % probabilities of transition from bull to bear and vice versa? Thank you very much
What a great video, thank you!!
Question: in your example of the stock market I think you used historical data to generate those bull-bull and bear-bear probabilities. Is it still a Markov process if the probabilities on the tree are derived from the past?
Ah great point, and it takes a bit of further consideration of what do we REALLY mean about "the past". It is more about ignoring the recent past. So I'm not looking at least week or a month ago in my predictions for next week. But your are right that while I made up the numbers in this example, they would have come from looking at some historical average over, say, decades.
A similar example might be weather. A markov model might be constructed that says given the weather today, what is the probability of the weather tomorrow, and it would be markov because it ignores what the weather was like yesterday or a week ago. However, the model might still build in historical climate data about what the weather is like generally.
Dr. Trefor Bazett thanks, that makes sense! Looking forward to the next one!
Thanks for video , it was mentioned Markov only based on “present” state however the transition probabilities themselves are based on historical data right? Just trying to get my head around that distinction.
Can't appreciate enough your videos. Plz keep making them, and I am second. hehehe
Will do! 2nd is still pretty good, haha:D
better than my teacher
You may also talking about how to find the stable status with linear algebra PDP^-1 which related to your series
Indeed, the next video is going to cover the connection to linear algebra although I won’t get to diagonalization for a while yet
Hey @Dr. Trefor Bazett Is this the Basics for Reinforcement Learning !!
Thank you Dr very well explained 👌
Do you have any videos about poison process and exponential distribution
Thank you! Yes I do plan to do a stats series at some point, but not just yet sorry:)
what would be the differences to a finite state machine and the states position?
Best
Crown of mathematics🥰🥰💝💝♥️♥️
haha, thank you Shah!
So basically we can consider markov chains as studying dependent probabilities?
Also, how is is bear and bull example markovian? As you mentioned, that using an old data set (past info.) is non markovian process.
But for that example of stock market you have considered past data does it not make , it as an example of non-markov method?
You are so amazing Dr.Trefor making all these heavy content accessible to everyone, I give my best wishes to the channel growth exponentially. 🎊😇 I am doing my best to promote this content to everyone !!
Thanks for your help Dewanik, really appreciate it!
Did you do the Transition Matrix video from the 'coming soon' note ? Thanks
Yup, should be in the discrete math playlist
A clear explanation, please, how can I contact you because I have some questions. Is there an email?
Which came first, the Markov Chain or the DFA/NFA?
So what kind of playlist would this Markov Chain belong to ?
Right now it’s in my discrete math playlist, but anything with probability or stats could talk about this.
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It's there to keep dry .. ? 🤨
In the final step, why did you multiply the probabilities of each branch and not adding them ?
I would like to know as well. I don't know the logic behind it.
I desperately need to see a movie with the character he described: A superhero with an hour memory span😂
Please make videos on vector calculus i.e. the calculus of vector fields. Please sir. There are no sources on TH-cam about this topic. Sir we don't know about any android software that can help us plot vector fields.
It's coming, starting in about 2 weeks!
@@DrTrefor Would you you recommend me a software on Android that can plot vector fields.
I would try navigating to wolframalpha on browser first I think, but I’m sure there are others
mark OV chain
Why are the superheroes randomly moving around the city?
This is counter-intuitive, the notion that experience has no value. Thanks.
shame about the audio
I dont think thats a cool superhero