Since many of you are asking about the calculation of left eigenvector (π)...Here are the equations:
from πA = π
0.2x + 0.3y + 0.5z = x
from π+π+π = 1
Hello, just trying to survive life right now... but same like my teacher at school - you didnt explain exactly how that you got the .41, .18 and .41 @7mins from the .30, 0 and .70
I did not do linear mathematics. It would mean a lot to me to understand those steps in minutes 7 to 8 in more detail , please
After watching this video. Nerd has been normalized. Amazing 😊
thank you sir
Great video! Many thanks
Oh God this was the series I actually needed! tnx bro!!!
Can we see you python script?
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Do more videos in stochastic
Very vice tutorial and Excellent video
I'm here from ddlc and I could not understand a thing until I saw this video. Dude is the most helpful guy on this site
I was trying to understand Evolution algebras and for that I needed idea of Markov chains. Beautifully explained. Thank you so much.
Hi, can you please share Some sources That talk abt Markov Chains? If you have a book that has a chapter about it, can you pls send some pictures? I am a highschool student and need to write a 15pages essay abt thuis topic.
Note for my future revision.
Markov Chain models a system that changes its status.
One important rule: the next status of the system only depends on its current status.
= serve pizza, serve burger or serve hotdog
= x, y, z
= Connected, Disconnected, Terminated, Active
Markov chain can be drawn as a state diagram.
Or written as a transition matrix.
State diagram represents all possible status and associated probabilities.
= represent the state diagram
= probability from one state to another
At equilibrium, the probabilities of the next status doesn't change any more. The probability of state at equilibrium = Stationary Distribution.
Let's call such equilibrium probability π.
Aπ = π
= Eigenvector of the matrix
= Probabilities of each status the system could be in, assuming equilibrium stage.
Using two equations:
A) Aπ = π
B) sum of probability is 1,
we can work out the value of π, i.e. the equilibrium probability
Alternatively, run a simulation.
A: Do all Markov Chain have a equilibrium state?
Q: Don't know... Go find out...
Q: Can I use for Conn Status?
I can "model" next status to be only depending on the current status. But the next status actually also depend on the previous status?)
A: Yes, I can. At per state level, the next status only depends on the current status, but the at the system level and at equilibrium, it "depends" on both the current and the previous state, because the current stated was "affected" by the previous state.
Any chance you'd share the python script?
very well explained. thank you for making this video
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please coments on jackknife technique for reducing the amount of biasedness of a biased estimator
This video was very helpful to understand the concept easily.... 😊
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Thank u sir
Thax alot sir, its really useful video for us.
Kindly make a video on application of the petrinets which use for Markov chain generation.
Sir thoda aur syllabus cover kra dijie markov chain ka
Sir your teaching method is very nice. Now, I like this topic after watching your video. Thank you Sir.
Very nice explanation sir.....
Concepts are cleared from root thank you sir...
sir pdf melega member banne se?
Excellent sir. Keep it up
Wonderful explanation. thankyou Sir
Good efforts & presentation to make the concept understand easily, keep it up. 💯
thanku sir for so easy explanation
Sir, their is one question in statistics from the topic "STOCHASTIC PROCESS" and i.e..
Ques- A sequence of experiment as performed , in each of which two function are tossed. Let Xn be equal to three numbers of heads in a repeanons of the experiment .
please provide solution of this question .
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Crystal clear explanation. Thanks!!
Thank you sir
Thankyou so much from the land of mountains, Nepal
Thank you so much
Very well explained thank you so muchh
Hata off to your efforts and thankyou ✨😀
Commulative 1 na aaye to
Agar phele commulative prob 0.1 and last 1 aaye to ist or last ka interval kya banega
Your voice is not coming properly
ThankYou So much sir♥️
Please provide NP agrawal book solution
Ka batayo hai bhaiiye kati samjh aagi mann me dharr choot gi
Thank you, nice explanations.
i have one question, for simulation we used different softwares, which method (discrete, continuous or Monte Carlo) is used in CST ( computer simulation technology)
Sir how to find out uncertainty related to data in Excel by MOnte Carlo Simulation