Exploring Applications of Markov Chains

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Homework Help Overview

The original poster is tasked with writing a report on applications of Markov chains, seeking examples beyond population dynamics and gambling. They express confusion regarding the definition and application of Markov chains, particularly in relation to a squash game scenario.

Discussion Character

  • Exploratory, Conceptual clarification

Approaches and Questions Raised

  • Participants discuss various applications of Markov chains, including queuing theory and statistical mechanics. Some express difficulty understanding transition matrices and their implications. Others inquire about the relationship between transition steps and time periods.

Discussion Status

Participants are exploring different applications and clarifying concepts related to Markov chains. Some have provided links to resources, while others are seeking further explanations on specific aspects, such as transition matrices and their meanings.

Contextual Notes

The original poster has indicated a need for simpler examples and is grappling with the complexity of the subject matter, particularly in relation to their current educational level.

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Basically I have to write a 2 page report briefly on some applications of markov chains, other than Population dynamics and gambling scenarios.

It would be great if someone could give me some ideas on relevant applications of these, fairly simple ofc :P The more indepth i read into markov chains, the less i understand :)

I've tried googling some stuff, I saw an application of markov chains in a squash game. I can see relevance as the shot your oponent makes next is based upon your shot now in the present. But surely the shot in the present is dependent on the shot your opponent played in the past, which to me isn't the definition of a markov chain. So i don't see how this can work.

But any other ideas I could use? :) Thanks
 
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Noone has any examples? :(
 
Awesome thanks :D
 
It turns out I am having trouble understandng transition matrices, especially from the link:

http://faculty.darden.virginia.edu/pfeiferp/Homepage/ModelingCustomersasMarkovChains.pdf

What does each row/column tell me in transition matrices? Using the example in the link, on page 9 would be helpful :) Also when moving up transition step matrices in the example, I see that the matrix P(1) already has its first column explained in the question, but for the 2nd, 3rd and 4th step transtion matrices I don't see where the 1st column in those come from.

thanks!
 
Last edited by a moderator:
Ok now I know what the rows and columns stand for. But I'm still unsure what the transition steps mean. In the example are the transition steps different periods in time, for set periods, and there are 5 states within each transition matrix for all the steps that happen within a period?

I also still can't figure out where how i achieve the P^2, p^3 etc. transiotion matrices, I THINK I have to right multiply it by a column vector, but how do I get the vector?

Thanks
 
/facepalm

Sorry I was being stupid, I see the P^2 matrices were just where P^2=PP.

Still though, I don't know what the matrices mean :P In terms of what the probabilities mean inside the matrix, and what the differnet steps show us.
 
You're forgetting perhaps the most important application of Markov chains - they power the search engine you've just used ... (Google) :). Google uses a Markov chain for its so called "link analysis". I recommend reading the book "Google's pagerank and beyond", or just, well, Googling it ;).

--------
Assaf
http://www.physicallyincorrect.com/"
 
Last edited by a moderator:
ozymandias said:
You're forgetting perhaps the most important application of Markov chains - they power the search engine you've just used ... (Google) :). Google uses a Markov chain for its so called "link analysis". I recommend reading the book "Google's pagerank and beyond", or just, well, Googling it ;).

--------
Assaf
http://www.physicallyincorrect.com/"

ooh nice, sounds very interesting, it seems its a book though, and I can't seem to find any free info of the math involved :P Could you briefly descibe the markoviness behind it? ;)
 
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  • #10
Scrub everything I asked above, now I would like to know stuff about google =P

I found this link here:
http://www.mathworks.com/company/newsletters/news_notes/clevescorner/oct02_cleve.html

It seems everywhere I look you need a few more years of education that what I have to understand, especially since the google owners devised the algorithm at the end of their degree =)

Has anyone here read the book 'Google's pagrerank and beyond'? Or knows anything to do with it and its relevance to Markov chains? Just briefly explained would be awesome! I'm sure there's a way to break down all of the hard notation into simpler english :).

thanks
 
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