Lecture #4: Solved Examples of Markov Chain using TPM (Part 3 of 3)

For Book: See the link amzn.to/2NirzXT
This lecture explains how to Solve the Problems of the Markov Chain using TRANSITION PROBABILITY MATRIX. #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...

[@chiragsahu115]

In our country, most doctorate degree prof doesn't know how to teach and they are not interested. But u are passionate and interested in teaching students. Thank u sir

[@DrHarishGarg]

Thank you dear... ❤🙏 its my pleasure to teach all of you.....

[@jimmartin6803]

You are so awesome Dr. Harish Garg, I was trying to learn from youtube how to solve these kind of problems and your playlist is super precise and very well explained. It took me 2 hours to find your playlist in youtube and I am not kidding you, I learned the way to solve these kind of problems faster than what it took me to find your channel and about 10x faster than the time I spent reading the theory and not understanding a single thing because there where no examples.

[@DrHarishGarg]

Many thank Jim for watching....kindly share with others too so that they will also learn in a simpler manner.... Happy learning.

[@cjs8037]

Sir now only I got clear on Markov chain problems, thanking you sir 🙏🙏🙏

[@DrHarishGarg]

Wow.... So nice and happy.... Happy learning....
Kindly share the videos with other so that they can also learn easily

[@cjs8037]

@@DrHarishGarg sure sir

[@hr.sharif0933]

You r great sir...!!!explanation r so much easy..
I request u... Plz sir,, make a video about classification of state...i hope u will solve this type of problems Step by step ❤...sir plz...plz...plz........

[@amritaajain2170]

Really explained very clearly. Thank you so much 🙏🙏🙏

[@DrHarishGarg]

Thanks for liking the content.

[@lingamguntamaneesha8801]

@@DrHarishGarg thank you sir u helped a lot

[@DrHarishGarg]

Its my pleasure..... Kindly share it with others too

[@shellypandey4316]

You are the life saver

[@DrHarishGarg]

It's my pleasure.... Watch others lectures and share with others

[@samuelmaina7130]

I think there's a mistake in the last example, you used q0 instead of q1

[@tvgathu9845]

i thought so too, i think he should clarify

[@yogendravishwakarma5592]

No need to comment for any mistake or error just learn the correct things whatever sir explained. Once you understand, you will be able to correct that by yourselves.

[@knowntounknowns5135]

Sir, I have a doubt.
In finding, P(X_3=3,X_1=1)
For this we have to find P_13^(2) .q_1(1) only right. But in your video, you found P_13^(2) .q_0(1). Why sir? I can't understand. Please explain.

[@DrHarishGarg]

Yes, you are right

[@mustufaabidi7905]

As always explanation in very nice and simple manner.

[@DrHarishGarg]

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

[@ravighimire8572]

Thank you Dr. Harish for clear explanation of Markov Chains (3/3 videos)

[@DrHarishGarg]

You're most welcome. Keep watching

[@ritu1071]

Thank you very much for such clear and crisp explanation. Really appreciate your efforts.👍

[@DrHarishGarg]

Its my pleasure .... Many thanks for watching.

[@cjs8037]

Really all those examples are very easy to understand ji...

[@DrHarishGarg]

Happy learning.... Keep sharing and watch other topics also in the easiest manner.

[@AnjuKumari-pt7dc]

Nobody taught like u
Thank u sir

[@bhushanbarde5041]

Thank u so so much! 🙏

[@zoyakim206]

Good explanation
Helping me a lot for sem exam preparations 👏👏

[@DrHarishGarg]

Happy to help... Keep watching

[@yogendravishwakarma5592]

Thank you sir for amazing lecture series. I am maths student but finding statistics very interesting & easy because of you. Thanks a lot sir.

[@DrHarishGarg]

It's my pleasure... Glad to hear that

[@hhss1763]

Excellent as usual!!!

[@chandruduk4309]

excellent videos...very clearly explained thanks a lot..

[@DrHarishGarg]

My pleasure.... Keep watching other videos too

[@kusumkumari6894]

Sir, can you please tell me do you provide tution for bsc statistics because I love the way you teach and I need your guidance in statistics so I am asking , please reply.

[@sssskhan3]

Very well explained with examples

[@DrHarishGarg]

Many thanks

[@dominicfosu2485]

I really love your teachings ❤🎉i wish i noticed your channel earlier😊😊😊😊

[@shruzzz]

thanks a lot sir! These markov chain lectures helped me a lot for my sem exams🙏.

[@DrHarishGarg]

Wow... Thats so nice.... Thanks for watching.... Keep in touch always

[@techhackz2897]

sir
please explain the last example in the comment
in the last example can we use conditional probability from the 2nd lecture of the this series

[@jaforislam3395]

Excellent explanation as always.

[@xplorer5384]

Thank you for all 4 videos
Clearly explained 👍

[@DrHarishGarg]

So nice of you... Glad you liked them.... You may Watch Lecture 5 and 6 too... Details as below:
Lecture 5: Stationary Probabilities: ruclips.net/video/zVCB49HYIxM/видео.html
Lecture 6: Chapman-Kolmogorov Equation & Theorem: ruclips.net/video/TptjhklRnqM/видео.html

[@surbhiswaraj6894]

Thanku for the video sir
It is very helpful 🙏

[@dhirsalvi8611]

Amazing explaination

[@adityaprasaddhal2462]

I'm getting confused in the last question 😕, it's some what similar to the 2nd method of solving Markov's chain

[@techhackz2897]

i have the same dought if we use the condition probability we get only 0.34

[@chiranjibejana1444]

Nice presentation professor

[@DrHarishGarg]

Many thanks. Keep watching and sharing

[@AdityaSingh-ql9ke]

Thanks a lot sir, once again

[@aa-gc3fg]

Thank you Soo much sir

[@DrHarishGarg]

My pleasure

[@gadwalabdulla974]

Thanks a lot sir❤️🫡

[@Ishorts-tc4sp]

Awesome sir

[@somu42]

i had a question to find unit fixed probability vector of the Matrix
how can i do that?

[@nilaislam5006]

Thanks

[@candy_br09]

I think there is a smalll mistake in last examle, first we should consider,, (q0*P)(1) * P^2(13)

[@maths457]

Sir P(X1=3) Kya hoga?

[@ritanshitrivedi4278]

Informative content

[@DrHarishGarg]

Thanks

[@cjs8037]

Sir please tell me what book have to purchase for basic stochastic processes

[@DrHarishGarg]

As such there are so many books on stochastic processes....if you want to buy then you can choose any one. All books are good.

[@DrHarishGarg]

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

[@sajidhasnat4457]

Thanks 👍😊

[@DrHarishGarg]

Thank you too

[@aparnamishra2972]

Sir last example me P(X3=1, X1=1)
Me
q1(1) use hoga?????

[@DrHarishGarg]

Yes

[@tvgathu9845]

in he last example; P(x3=3,x1=1) , shouldnt we first calculate q1(1)?

[@DrHarishGarg]

Yes, you need to compute q1(1)...

[@vinayaksinghal]

@@DrHarishGarg sir video description mein update kardiya karo please

[@user-rd1ok6zs3f]

WOW

[@shrawankumarpatel2148]

🌷🌷🌷

[@shortWM]

[@DrHarishGarg]

Many thanks....
Join my telegram link of Probability and Statistics
t.me/gargProbability

[@santhoshnaidukoribilli]

hi sir can you give us solution form transition matrices
Let Xn ∈ Z≥0 be the amount of water (say measures in the unit of hundred
million gallons) in MacRitchie Reservoir at noon on day n. During the 24 hours period beginning at
this time, Yn ∈ Z≥0 units of water flows into the reservoir. The maximum capacity of the reservoir
is 5 units, and excessive inflows are spilled and lost. Just before noon on each day, exactly 1 unit of
water is removed if the reservoir is non-empty, and no water is removed if the reservoir is dried up.
Assume that Y1, Y2, . . . are iid discrete RVs, where1
Pr(Y1 = 0) = 1\4
, Pr(Y1 = 1) = 1\6
,
Pr(Y1 = 2) = 1\12
2
, Pr(Y1 = 3) = 1\6
, Pr(Y1 = 4) = 1\3
.
In fact, {Xn}
∞
n=1 forms a DTMC. Write down the transition probability matrix of the DTMC

[@mohinishendye1040]

👍👍

[@akshatgoyal9113]

Last example is wrong. We need to find q1 of state 1 by using q1=q0*p and than use state 1 from 1 as our probability

[@DrHarishGarg]

Thanks

[@akshatgoyal9113]

@@DrHarishGarg btw thanks sir for the great videos learnt it off you only.

[@DrHarishGarg]

My pleasure dear always.... Keep watching other videos too and share with others. Thanks and cheers

[@wylercambod2955]

Zero to jero

[@celebrities01]

Thank you so much, sir ji🙏

[@sunandapatra2774]

Thank you so much sir.

[@DrHarishGarg]

My pleasure.... Keep watching and sharing