Intro to Markov Chains & Transition Diagrams

[@Darkev77]

The most thorough and clear explanation ever. Can't wait for the next video!

[@DrTrefor]

Thank you! Follow up video coming next week:)

[@paulkaranja9264]

You just made my life better all the way in a small country found in Africa called Kenya. Thank you.

[@SAAARC]

A subscription to your channel is the gift that keeps on giving.

[@DrTrefor]

Glad you're enjoying!

[@bijanghofranian5782]

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.

[@joevanderstelt8143]

Subscribed to this channel after about 30 seconds.... amazing

[@MrChinook1991]

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

[@mapmap123456]

one of the best explanations about Markov chains on youtube. thank you

[@franklinoduro7274]

The best math video so far on Markov chains

[@MrBitviper]

amazing explanation. you have a knack for making these difficult topics understandable thank you so much for this

[@DrTrefor]

Glad it was helpful!

[@PrpleHatMan]

A very well presented and insightful lesson. Thank you for the 2 part explanation!

[@kaniki_the_problem]

You a natural. Thanks , preparing for Risk Modeling and Survival analysis actuarial exam

[@aqeelzeid24]

I watched around 5 videos , this explained it the best !! thanks alot

[@abdullahbinnaeem9502]

Never seen such explanation before. Amazing Sir

[@alfonsorodriguezruiz9007]

Greetings teacher. Thanks for sharing your amazing classes.

[@byronwilliams7977]

Love your videos man, keep up the great work. You and Presh Talwalker are the best.

[@camsasuncion]

I really appreciate the clarity of the explanation. I am now a subscriber and a fan! Thank you.

[@DrTrefor]

Thanks for the sub!

[@thedigitalphysicist]

A simple explanation of something that could be very complexly understood. Thank you, Dr. Bazett

[@ridwanhusainishraq]

what a sarcasm.

[@imdadood5705]

Very clear explanation with easy examples. Thank you!

[@Ferdimitry]

Perfect. Dr. Trefor you are the best!

[@DrTrefor]

Thank you, glad you enjoyed!

[@priyankashaw3010]

really helped to understand this concept in the first go

[@genuinebombayite1966]

This was such a wonderfully clear explanation. Thank you so much!

[@DrTrefor]

Glad it was helpful!

[@genuinebombayite1966]

@@DrTrefor Not to get too greedy, but can you do one on Hidden Markov models? Thanks a bunch again!

[@HA-zd5gx]

I'm happy tha I found this channel. thank you!

[@emrahakcay]

I wathed many videos, but I understand mc at this video. Perfect explaination bro hank u

[@eleazertham5033]

Thanks I needed this for my upcoming exams :-)

[@thepresistence5935]

Please don't go to MIT, Standford at all, be here. We need you.

[@catcen9631]

incredible video with a super clear explanation!

[@QuantiFaiPortfolio]

Love this!!!

[@jesseluinstra1192]

Thank you. This was a very good explanation

[@randa_alwadi]

Your explanation is super!

[@eyakhamassi8748]

Thank you soo much this is the best explanation ever❤❤❤

[@satyambhardwaj2289]

absolute delight.. exactly at the time I wanted it for an AI implementation

[@ipshitaghosh2656]

Rare moments.. when you understand in the first go.

[@freedmoresidume]

Crystal clear explanation , thank you Dr.

[@imad_uddin]

This was strikingly clear and fresh. Loved it!

[@sjchsbc]

Thank you for making this.

[@anishjoshi1999]

Thank you so much Doctor 😍

[@SonaliAcharya-ry8xg]

Very good explanation 👏

[@TPLCreationLoft]

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?

[@maulikjadav9673]

Awesome explanation, Thank you sir. 👍

[@Storyguy]

Great Sir.... the explanation is ridiculous ❤❤

[@drachenschlachter6946]

A very good explanation!

[@sanklink]

Awesome explanation!

[@Shaunmcdonogh-shaunsurfing]

Fantastic explanation

[@Big_Mo_Zak]

Very well explained.thank you

[@jenishghimire6678]

So good way of explanation

[@TheMostafa5000]

amazing explanation.

[@rashasulieman2525]

Wow! Just beautiful! Love way you explain things!

[@DrTrefor]

Thank you so much!

[@SnowyMan95]

Great video. Thanks!

[@mcyz7871]

awesome job

[@HylianEvil]

If only the next video was out so I could make a Markov chain to predict when the next video will be out

[@DrTrefor]

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;)

[@othmanaljbory3649]

عاشت الايادي استاذ شكرا جزيلا بالتوفيق والنجاح

[@continnum_radhe-radhe]

Wow 🔥🔥🔥

[@giovanniberardi4134]

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

[@sebaaismail1951]

Thank you dr, yours vidéo are usefull.

[@DrTrefor]

Thank you!

[@joicet5230]

nice explanation for a beginner like me

[@admiralhyperspace0015]

Can't appreciate enough your videos. Plz keep making them, and I am second. hehehe

[@DrTrefor]

Will do! 2nd is still pretty good, haha:D

[@SHAHHUSSAIN]

Crown of mathematics🥰🥰💝💝♥️♥️

[@DrTrefor]

haha, thank you Shah!

[@peachiichaii6375]

oh my for this was so helpful thank you so much

[@melroyrodrigues5975]

Thank for the wonderful video. It had a really crisp and clear explanation. I had a doubt. You mentioned that Markov chains don't use any information from the past. So in the bull and bear example, it should have been 50% chance of a bull staying a bull and 50% of bull changing to a bear (Very similar to your city-train example where you inspect the possibilities at any given station) So why did you use past results of 75% chance of a bull staying a bull into the computation? Thank you!

[@OfferoC]

Awesome thanks

[@ccuuttww]

You may also talking about how to find the stable status with linear algebra PDP^-1 which related to your series

[@DrTrefor]

Indeed, the next video is going to cover the connection to linear algebra although I won’t get to diagonalization for a while yet

[@Salvador-xy5es]

maan you are awsome THANK YOU!!!

[@Sam-fv4xq]

better than my teacher

[@muhammadneanaa1611]

Thanks!

[@thetutorialdoctor]

Excellent

[@mattpecevich3749]

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?

[@DrTrefor]

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.

[@mattpecevich3749]

Dr. Trefor Bazett thanks, that makes sense! Looking forward to the next one!

[@sukanthenss914]

Hey @Dr. Trefor Bazett Is this the Basics for Reinforcement Learning !!

[@CamEron-nj5qy]

A superhero who can't fly but has a cape 😂

[@lazertroll702]

It's there to keep dry .. ? 🤨

[@bonecircuit]

what would be the differences to a finite state machine and the states position?

[@kdewanik]

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 !!

[@DrTrefor]

Thanks for your help Dewanik, really appreciate it!

[@jace3789]

Did you do the Transition Matrix video from the 'coming soon' note ? Thanks

[@DrTrefor]

Yup, should be in the discrete math playlist

[@Tiredprincessss]

THANK YOU

[@5haik_Muzammil]

But for that example of stock market you have considered past data does it not make , it as an example of non-markov method?

[@sambhavgupta4653]

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.

[@georgesalexandrebajk8223]

Thank you Dr very well explained 👌 Do you have any videos about poison process and exponential distribution

[@DrTrefor]

Thank you! Yes I do plan to do a stats series at some point, but not just yet sorry:)

[@BlackCodeMath]

Which came first, the Markov Chain or the DFA/NFA?

[@tuongnguyen9391]

So what kind of playlist would this Markov Chain belong to ?

[@DrTrefor]

Right now it’s in my discrete math playlist, but anything with probability or stats could talk about this.

[@ungoyboy2006]

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.

[@twishanuaichroy1938]

Best

[@physicslover1950]

Please make videos on vector calculus i.e. the calculus of vector fields. Please sir. There are no sources on KZbin about this topic. Sir we don't know about any android software that can help us plot vector fields.

[@DrTrefor]

It's coming, starting in about 2 weeks!

[@physicslover1950]

@@DrTrefor Would you you recommend me a software on Android that can plot vector fields.

[@DrTrefor]

I would try navigating to wolframalpha on browser first I think, but I’m sure there are others

[@binshebah]

In the final step, why did you multiply the probabilities of each branch and not adding them ?

[@mryup6100]

I would like to know as well. I don't know the logic behind it.

[@ayamohammed4355]

A clear explanation, please, how can I contact you because I have some questions. Is there an email?

[@jacobfertleman1980]

I desperately need to see a movie with the character he described: A superhero with an hour memory span😂

[@ronaldmarcks1842]

This is counter-intuitive, the notion that experience has no value. Thanks.

[@aashsyed1277]

mark OV chain

[@metalhamster14]

shame about the audio

[@twilight1176]

I dont think thats a cool superhero