Lecture #3: Solved Problems of the Markov Chain using TPM (Part 2 of 3)

[@Sandip6521]

This is the best explanation that i ever found on utube !! After 2 days i have AI exam u save my many hours

[@DrHarishGarg]

Its my pleasure ... Kindly share with other students too so that they will also learn with the easiest manner. Keep watching anc sharing

[@ramakantchhangani8627]

You saved my End Semester Exam!

[@DrHarishGarg]

My pleasure

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@Vance Joaquin instablaster =)

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[@LaibaKhan-i6z]

What a great video tbh. Cleared all my queries.

[@chiranjibejana1444]

Excellent lecture professor

[@DrHarishGarg]

It's my pleasure

[@pothugantideepika2939]

Thanks to the author of the video

[@fatimabatool2357]

it's very good explanation

[@zaheeruddin1928]

Thank you very much sir🥰🥰

[@im.endangered]

lov u sir. very clear lesson

[@mamtasinghchauhan9353]

Thanks a lot .... it helped immensely

[@shek8560]

Super explained sir❤️❤️❤️❤️❤️❤️

[@DrHarishGarg]

Thanks.... My pleasure ... Happy learning

[@gobezeassefa1437]

What a great video!!! Thank you!

[@DrHarishGarg]

Its my pleasure.... Keep watching other videos too... I am sure you will enjoy again.

[@gobezeassefa1437]

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[@spiritwithdevika777]

Thank you Sir...helped a lots

[@saikeerthana4809]

Really osm ❤️

[@DrHarishGarg]

My pleasure.... Keep sharing

[@zoyakim206]

Best explanation ever 😊 Thank you

[@DrHarishGarg]

You're welcome 😊

[@mesydiahariani9570]

great explanation. Thanks

[@DrHarishGarg]

My pleasure.... Keep watching and sharing.

[@ahmedmostafakhalil3529]

Nice explanation sir!!!

[@yaswanthcreatives3245]

Sir 1st example ans 5/16 sir according to the previous video

[@rahulkamboj4804]

sir nice lectures helps me a lot .....thx

[@DrHarishGarg]

My pleasure always... Keep watching and sharing with others too

[@DrHarishGarg]

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

[@ritanshitrivedi4278]

Very nice explanation

[@marvellousstudiomovies8125]

Thanks alot sir

[@anirudhkundu722]

In example 10, if the individual "qo" was not given we still could have solved for all the parts only using "P" matrix? is that right?

[@DrHarishGarg]

No.... Without q0, we can compute only transition probabilities but not the state probability..... If we want to know the probability of states then q0 must be needed aa discussed in Lecture 2,3,4 respectively

[@mustafadagstan8215]

3:48 Shouldn't it be P(0,3) bcs we are starting from 0 to 3

[@exe-678]

We are starting from 1

[@maths_jekkoi]

Thankyou profesor

[@souravpaul_per]

Thanks sir ..❤️

[@kavyasharma8743]

is this the case of absorbing chains?

[@armarmarm2809]

can u do tutorial of MARKOV CHAIN LOTTERY..thank you Dr. Harish Garg

[@ROKADEADITYA]

Thanks

[@yaswanthcreatives3245]

Sir y don't we use formula q° that one

[@sssskhan3]

Nicely explained

[@DrHarishGarg]

Thanks for liking. Keep watching the next part also

[@pvschaitanya9445]

when do we use the formula (q0 * p^n) and just p^n

[@HariSakshi_Khatiwada]

When it is not mentioned which the previous state is i.e. we just know we want to go to "this" state, we'll use q0. Here, multiplying with q0 makes the obtained matrix the sum of probabilities as "A" to "A" + "B" to "A" +... in first location, "A" to "B" + "B" to "B"+... in second and so on. (Hope this didn't make it more confusing. If it did, skip this second line) When we know we want to go from "this" to "that" (both are mentioned) , we'll use p^n only.

[@LEARNWITHPANDA]

Prof. Harish, which software are you using to make the slides, PowerPoints? I like the pen. Thank you very much

[@DrHarishGarg]

Yes

[@kashbhalla4363]

sir can you provide these slides?

[@abhijeetkumarmaurya4697]

How can I got formula qn = q0P^n ?

[@DrHarishGarg]

Use q(n) =q(n-1)P , you can derive q(n) =q(0)P^n

[@abhijeetkumarmaurya4697]

@@DrHarishGarg sorry but ,then how can I got this sir...

[@DrHarishGarg]

Ok No problem, i will elaborate For example, if n=3 q(3)=q(2)P = [q(1)P]P =q(1) P^2 = [q(0)P] P = q(0) P^3

[@abhijeetkumarmaurya4697]

@@DrHarishGarg thanku sir

[@DrHarishGarg]

My pleasure... Happy learning

[@mayankaggarwal6447]

sir best explanation , but sir please work on your english , your channel will automatically grow

[@maths_jekkoi]

Prof Is this true P{X2=1 , X3=2|X1=0}=P{X3=2|X1=0 ,X2=1}=P{X3=2|X2=0}

[@mohammedalsubaie3512]

Dear Dr. Harish do you see machine Learning, deep learning and reinforcement learning techniques as OR methods to solve markov process models please? Or they are under another category not OR?

[@DrHarishGarg]

All are under the advanced operations research

[@smartbuyerbay6418]

Explanation is super but video is not clarity

[@DrHarishGarg]

Thanks for the suggestion... I will take care

[@mohinishendye1040]

👍👍

[@HimanshuSharma-go5jb]

If we are talking about 4 steps, then isnt it qoP⁴ as we done in session no 1