Markov Chain Conditional Expectation

AI Thread Summary
The discussion centers on clarifying the equations related to Markov chains, particularly the equality of certain expressions. Participants emphasize the Markov property, which states that the probability of the current state depends only on the immediately preceding state, not on earlier states. There is a request for intuition on how this property applies to the equations in question. Additionally, a query is raised regarding the conditions under which the probability P(X_{n}=j|(X_{0}=j) holds true. Understanding these concepts is crucial for grasping the underlying principles of Markov chains.
tunaaa
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Hello, in relation to Markov chains, could you please clarify the following equations:

j5b987.jpg


In particular, could you please expand on why the first line is equal. Surely from
gif.gif
, along with the first equation, this implies that:

2nk01nr.gif


I just don't see why they are all equal. Please could you provide some intuition on this. Thanks
 

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tunaaa said:
Hello, in relation to Markov chains, could you please clarify the following equations:

j5b987.jpg


In particular, could you please expand on why the first line is equal. Surely from
gif.gif
, along with the first equation, this implies that:

2nk01nr.gif


I just don't see why they are all equal. Please could you provide some intuition on this. Thanks

The Markov property is where the probability of the present state n is conditional only on the probability of the immediately preceding state n-1. There's no dependence on prior states n-i for integer i; 1<i\leq n.
 

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OK, sure - but how is that fact relevant to understanding the above equation? Thanks.
 
tunaaa said:
OK, sure - but how is that fact relevant to understanding the above equation? Thanks.

Given the Markov property, if P(X_{n}=j|(X_{0}=j), then under what two conditions could this be true?
 
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