Lecture #1: Stochastic process and Markov Chain Model | Transition Probability Matrix (TPM)

For Book: See the link amzn.to/2NirzXT
This video describes the basic concept and terms for the Stochastic process and Markov Chain Model. The Transition Probability Matrix is also described with Illustrative examples.
#OptimizationProbStat
Other videos ‪@DrHarishGarg‬
Lecture 1: Markov Chain & TPM: • Lecture #1: Stochastic...
Lecture 2: Problems using TPM Part 1: • Lecture #2: Solved Pro...
Lecture 3: Problems using TPM Part 2: • Lecture #3: Solved Pro...
Lecture 4: Problems using TPM Part 3: • Lecture #4: Solved Exa...
Lecture 5: Stationary Probabilities: • Lecture #5: Stationary...
Lecture 6: Chapman-Kolmogorov Equation & Theorem: • Chapman-Kolmogorov Equ...

[@Vaibhav_Hanbar]

03:58 Understanding stochastic process and Markov chain model with transition probability matrix 07:56 Stochastic process and Markov Chain Model 11:54 Stochastic processes are not constant but change over time, represented by finite or countable infinite values. 15:52 Markov chain depends only on the previous state, not on the remaining ones. 19:50 Transition Probability Matrix (TPM) is a square matrix where each row represents past positions and each column represents present positions. 23:48 Stochastic process and Markov Chain Model explained with Transition Probability Matrix (TPM) 27:46 Construction of Transition Probability Matrix (TPM) 31:40 Understanding Transition Probability Matrix (TPM) in Stochastic process and Markov Chain Model Crafted by Merlin AI.

[@_blessed_mess_]

I was so worried about this part of the syllabus until I found this video. The topic is well explained that caters perfectly to a student's understanding.Thank you so much!!!

[@monirulhaque8830]

The best lectures for stochastic process on youtube.

[@DrHarishGarg]

Many thanks for the appreciation... Keep watching other videos too

[@amanifadiagarroudji8976]

I agree thank you professor

[@khadijanafees7426]

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[@DrHarishGarg]

My pleasure.... Keep watching other videos too., definitely you will learn more. Happy learning

[@mustafamarvat863]

These are the most difficult concepts to understand in the whole of probability theory but your style of teaching make it very easy to grasp it.

[@StudyStuff-pk7gv]

This video is truly a life saver. I have an exam tomorrow, and this video explained me every basic thing about markov chain, process, and tpm, in such an illustrative way. THANK YOU SO MUCH FOR UPLOADING IT!

[@SanjayVerma-mf4ch]

Giving relevant example is most beautiful part of your lectures 👍

[@mukundachoudhury5785]

In one line Great Teaching skill.100% understable.. Really appreciate your teaching skill.

[@DrHarishGarg]

Its my pleasure. .. Keep watching other parts too

[@fouziakhan3356]

Wonderful Video. I have seen all your videos and I would say the only Dr. on KZbin with Zero Dislikes !!

[@DrHarishGarg]

Many many thanks.

[@Brrriana]

thank u! the breakdown of the topic is much easier to understand especially when the examples are well explained in a simple manner and is not complicated

[@taruvarmittal1484]

Finally something went into my head for this topic ! Thanks a lot for this video !

[@DrHarishGarg]

You can watch other lectures also... You get 100% result....

[@TeenaMalhotra]

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[@DrHarishGarg]

Thanks.... Keep sharing too with others

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[@DrHarishGarg]

Thanks .... Kindly subscribe and watchjng other videos too. You will enjoy and definitely learn. Happy learning

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[@DrHarishGarg]

Dear topic please

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[@AdityaSingh-ql9ke]

great lectures sir...plz continue to help students...as college teachers are not interseted in teachinf...espeacially in govt. college

[@sssskhan3]

Clearly explained the concept by professor!!

[@DrHarishGarg]

Many thanks

[@yorgundemokrat4615]

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[@VARUNKUMAR-Up]

jo IIT ke professor nhi samjh pae aap kr diye🙏

[@DrHarishGarg]

Thanks for watching.... You may watch other videos for easy learning

[@dr.sudanjha9505]

Excellent exemplifying examples. Keep going Dr. Garg

[@DrHarishGarg]

So nice of you. Keep watching and sharing other videos also

[@ClaudiaOkoree]

Dr Harish, thank you so much. I'm definitely subscribing

[@nick20_s]

Thank you so much sir.... I'm grateful to you extreme from my heart. I've always considered stochastic as a difficult paper but this lecture really helped me.🙏🙏

[@DrHarishGarg]

Its my pleasure.... Whole probability and statistics subject is very easy... Watch others videos also for the simplest way to learn.

[@girishaa904]

very clear explanation. Thank you.

[@RanjeetSingh-ul2rc]

Thank from bottom of my heart

[@DrHarishGarg]

Its my pleasure..... You may watch lectures 2-5 also related to Markov Chain... I guarantee that you again learn full this topic

[@vaishaliranjan4862]

Thank you so much for giving us the example of ladies using a lipstick. Tht was easiest part to understand overall concept of tpm

[@khushbootiwari6160]

Thank you so much sir .... After so much of confusion finally its clear how can i make TPM. Would love to watch the further series by you😊

[@DrHarishGarg]

My pleasure..... Keep watching other video too to learn more concept and topics

[@georgepefanis9278]

Continue the great work. Thanks for the help🙏

[@DrHarishGarg]

My pleasure.... Check and watch the other beautiful lecture related to Markov Chain as Lecture #2, Lecture #3, Lecture #4 and Lecture #5

[@unnatishukla8513]

awesome collection of exampleas, thankyou sir, great video

[@learning4156]

Very well explained professor

[@DrHarishGarg]

Thanks for liking

[@michaelmarino4229]

Nice explanation from Sri Lanka

[@cjs8037]

Really it is very easy to understand, thank you so much ji 🙏🙏🙏

[@DrHarishGarg]

Many thanks.. Kindly share with others. Spreading education

[@cjs8037]

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[@kneebrain9298]

very helpful! thank you so much teacher ❤

[@celebrities01]

Thank you so much,Sir..❤❤ Very clear explanation and you made it easy to understand.

[@veenarajshekhar8145]

Excellent explanation about the subject... Thank you sir

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[@ashaynaik7540]

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[@TeenaMalhotra]

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[@DrHarishGarg]

Thanks...for watching... Keep watching other video too... I am sure you will enjoy and learn alot

[@hoytvolker3]

Thanks so much Dr harish

[@DrHarishGarg]

My pleasure ... Keep watching and learning Happy learning

[@User_defined-577]

Great explanation sir ❤‍🔥

[@rutujajadhav9407]

Tomorrow will be my exam... Thank you sir for helping me... Everything you clear 🥰🥰

[@DrHarishGarg]

That's my pleasure... Keep sharing with other students as well... Happy learning g and best of luck

[@jairamsinghal1424]

Excellent lecture

[@DrHarishGarg]

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[@himangshuborah2889]

Thank you sir..for helping us...

[@Ankit-vq9bh]

awesome video.

[@iamusmanshabbir142]

29:40 kya idhr apne glt nh llihka ? loss ki jgaha gain ana tha or gain ki jgah loss ana tha... Between your teaching method was wonderful Hats off for your efforts !!

[@nishakatakval2998]

Well explained sir 👌👌👌👌

[@DrHarishGarg]

Thanks

[@abrarajazwani2942]

This is so nice and delicately explained. MashaAllah, keep the good work up!

[@DrHarishGarg]

Thanks dear... I hope you watched other videos too and share as well with others

[@LokeshBabuTeku]

nice explanation

[@sai_teja.]

Sir, very well explained tqsm

[@DrHarishGarg]

Thanks ... Kindly share with other too ...

[@sai_teja.]

Sure sir👍👍👍

[@shraddhaprabhakar4365]

Thank you so much sir, really very helpful

[@DrHarishGarg]

Many thanks for watching ... Watch other lectures on markov Chain.. You will definitely enjoy and learn alot from the series of 5 lectures on Markov chain

[@shraddhaprabhakar4365]

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[@utkarshdubey6812]

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[@arify.1601]

thanks for lecture.

[@DrHarishGarg]

My pleasure.... You may watch lecture #2-#5 for more interesting one to learn Markov process

[@komalzehra3380]

Great lectures, plz upload more lectures

[@DrHarishGarg]

You can found the others lectures from my channel. All are available.

[@komalzehra3380]

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[@ramanuiet]

Great explanation sir

[@poojaprasad5653]

vry nice explanation sir....thanks a lot

[@DrHarishGarg]

Thanks .... My pleasure. Keep watching other content too and share with others.

[@aps2412]

Thank you for the superb content. Can you please also create videos on Martingales theory. It is so tough to understand now. Thanks

[@DrHarishGarg]

Will try

[@abhishekgaurav6928]

Nice explanation🙏

[@DrHarishGarg]

Thanks a lot... Keep watching and sharing with others too

[@sourabhdhiman5189]

Thanks a lot sir...

[@DrHarishGarg]

My pleasure... Happy learning

[@vasantkumarmishra3537]

Thnx a lot for such nice videos ,I requeste 2 u that please start lecture series on csir+ gate syllabus

[@DrHarishGarg]

Already uploaded.... Check the seperate playlist available... More than 250 videos uploaded on CSIR and GATe exams

[@madhavramanathan7555]

Thanks a lot, this really helped me

[@DrHarishGarg]

My pleasure .. Thanks for watching... Keep watching other parts too for full topic

[@NABARUNCHAKRABORTY-gu2lr]

Sir if you can make a video on Brownian motion before GATE 2024...it will be very helpful for us

[@amanifadiagarroudji8976]

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[@vinitakumari5394]

Very well explained tq sir 🙏

[@DrHarishGarg]

My pleasure

[@abhishekshukla2974]

Very helpfull.

[@Songvbm]

how do you get the probabilities at 15:06 ?

[@ritanshitrivedi4278]

Thank you so much sir🙏

[@DrHarishGarg]

Its my pleasure.... Watch other parts also on Markov chain... I am sure that you will learn full on the Markov chain topic.... Share with others also

[@sai_teja.]

😄

[@kalyben285]

Dr. Harish Garg when are you releasing videos on Discrete Time Markov Chain(DTCM) and Continuous Time Markov Chain (CTMC)😢

[@gauravsuthar7699]

Nice one

[@cjs8037]

Sir can you tell me the question of that TPM example which you have given sir, because I don't know about that values, that how it may came... That 6x6 matrix values I need sir

[@fredrickkimani8002]

I think the values where given, u just check from the transition diagram

[@kheramathshub4348]

Thanku sir.... Hats of u

[@DrHarishGarg]

thanks

[@knowntounknowns5135]

Sir, i have a doubt. Is P(x_3=2,x_2=3,x_1=3,x_0=2) and P( x_3=2/x_2=3,x_1=3,x_0=2) and Px_3=2/x_2=3) all are same?

[@DrHarishGarg]

If it belong to Markov chain, then only 2nd and 3rd are same. While in terms of conditional probability, all are different.

[@Dmdusjsnwn123]

Sir also take a class above positive recurrent and null recurrent

[@DrHarishGarg]

OK. I will do

[@AbhishekPatel-fs3bt]

awesome❤

[@DrHarishGarg]

My pleasure. ...keep watching

[@karthikaikannan4766]

Very useful

[@DrHarishGarg]

Many thanks ... Keep watching other parts also to learn full Markov chain concept

[@devyaninitturkar8662]

Excellent

[@DrHarishGarg]

Thanks

[@srishtiasthana2838]

sir ur vedio is very nice but the needs to be more loud.

[@lathabathyala1]

Sir,I have 1 problem on marcov transition probability matrix can you please solve?? Can I share that problem??

[@aa-gc3fg]

Thank you

[@Dmdusjsnwn123]

Class above mean recurrence time ??

[@archanawaghmare1730]

Sir which of the books use

[@DrHarishGarg]

For Book: See the link amzn.to/2NirzXT

[@rohanbiswas3388]

Thank you very much Sir. These 5 videos of Stochastic Process helped me a lot. Need another help - Can you or anybody suggest me a good book of Stochastic Process? From where, I can solve some problems myself. Thank you in advance.

[@DrHarishGarg]

For Book: See the link amzn.to/2NirzXT

[@rohanbiswas3388]

@@DrHarishGarg Thank You .

[@sameennoor9678]

I just say best of all

[@DrHarishGarg]

Thanks and My pleasure... Keep sharing with other students too so that they will also learn with easiest manner... Happy learning...

[@DD-bl8ql]

Thanks

[@chinmayarao2464]

Thank you sir

[@DrHarishGarg]

Welcome

[@gourabchanda8328]

Sir Thanks a lot for the video series its really helpful . I have one question how we are finding the probability in the TPM . for example in cola example it's 90% that they will not switch to other brand but I want to know how we are getting 90% in real life problems??. Thank you

[@DrHarishGarg]

Thanks. Its a priori probability.... If it is not given then you can compute it based on distribution function (concept of random variable).

[@sangitpradhan6818]

the sum of the coloumn should be 1 right? or can we assume any?

[@DrHarishGarg]

For TPM, sum of each row is 1, column sum may or may not be 1

[@AmitKumar-pt7hb]

sir can i mail u my assignment question of markov chain..can't do it?

[@sivakrishnadasari7470]

Excellent Sir...Can you please share the PPT

[@DrHarishGarg]

Please wait

[@LetzzRockk]

Wowowow

[@Aamirphysics]

Plz deliver full playlist plz

[@DrHarishGarg]

See the full videos on Markov Chain from below playlist Probability & Statistics: kzbin.info/aero/PLO-6jspot8ALyrofp0fWp0fOb19NcFS4V

[@nitishgupta169]

Sir , please upload Continuous time Markovian Chain , Limting Distributions, Birth Death process , Queueing Model M/M/1, M/M/c

[@DrHarishGarg]

Limiting Distribution is already available... See Steady State Probability for Limiting Probability (lecture 5)

[@nitishgupta169]

@@DrHarishGarg found it sir🙂

[@trusted_owners]

hats off

[@ShivamGupta-cx3hy]

😄😄😄😄😄 Thank You Sir

[@abhijeetkumarmaurya4697]

Sir please suggest a book for this topic...

[@DrHarishGarg]

For Book: you may go through amzn.to/2NirzXT

[@abhijeetkumarmaurya4697]

@@DrHarishGarg thanku sir for your reply...

[@sivaranjani2504]

Pls will u give me a 5*5 matrix examples in tpm

[@DrHarishGarg]

6x6 matrix is already given in the lecture...

[@y0ake77]

11:35

[@rahulpatle2006]

👌 👍

[@khadijanafees7426]

i want to ask some queries related to HMM can you plz send me your email?

[@LetzzRockk]

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Bal tu

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Speak hindi please