Dynamic transition probability matrix in markov chains

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Dynamic transition probability matrices in Markov chains allow for variable transition probabilities based on factors like population dynamics. This concept contrasts with traditional models that assume constant probabilities between states. The discussion highlights interest in non-homogeneous or time non-homogeneous Markov chains, which accommodate these changing probabilities. There is a call for systematic studies and references on this topic, indicating a gap in available literature. Understanding these dynamic models could enhance the analysis of complex systems where transition probabilities are not static.
pyrole
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I am intrigued by the idea of changing/dynamic transition probability matrix instead of the assumption of constant transition probability between states.

For eg. Probability of population migrating between 2 states might not remain constant and can be a function of its population or some other factors. Is there a systematic study of this particular category of dynamic or changing transition probabilities in markov processes.
 
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You're interested in a topic called "non-homogeneous Markov chains" or "time non-homogeneous Markov chains".

I don't know a good reference that introduces this topic and I don't claim to be able to introduce it myself!
 
The standard _A " operator" maps a Null Hypothesis Ho into a decision set { Do not reject:=1 and reject :=0}. In this sense ( HA)_A , makes no sense. Since H0, HA aren't exhaustive, can we find an alternative operator, _A' , so that ( H_A)_A' makes sense? Isn't Pearson Neyman related to this? Hope I'm making sense. Edit: I was motivated by a superficial similarity of the idea with double transposition of matrices M, with ## (M^{T})^{T}=M##, and just wanted to see if it made sense to talk...

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