(Created page with "Category:Walther MA271 Fall2020 topic2 =Restrictions of Stationary Distribution In the last section, it is emphasized that steady-state vectors can be derived only with...")
 
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<center>[[File:Statediagram.jpg|500px|thumbnail]]</center>
 
<center>[[File:Statediagram.jpg|500px|thumbnail]]</center>
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We can also write that in matrix form:
  
 
<center>[[File:Transitionmatrix2.jpg|500px|thumbnail]]</center>
 
<center>[[File:Transitionmatrix2.jpg|500px|thumbnail]]</center>
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Going back to the Python program from before, when the initial state is <math>[1, 0, 0]</math>,
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<center>[[File:Pythondemo7.jpg|500px|thumbnail]]</center>
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<center>[[File:Pythondemo8.jpg|500px|thumbnail]]</center>
  
 
[[ Walther MA271 Fall2020 topic2|Back to Markov Chains]]
 
[[ Walther MA271 Fall2020 topic2|Back to Markov Chains]]
  
 
[[Category:MA271Fall2020Walther]]
 
[[Category:MA271Fall2020Walther]]

Revision as of 03:35, 6 December 2020


=Restrictions of Stationary Distribution

In the last section, it is emphasized that steady-state vectors can be derived only with regular matrices. What if these vectors are not regular?

Consider a periodic Markov chain:

Statediagram.jpg

We can also write that in matrix form:

Transitionmatrix2.jpg

Going back to the Python program from before, when the initial state is $ [1, 0, 0] $,

Back to Markov Chains

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