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A Markov Renewal Process (MRP) is a stochastic process that consists of a (possibly infinite) set of paired random variables \(\{(X_n, T_n)\}_{n=0}^{\infty}\). To build intuition, we can think of the \(X_n\) variables as observations and \(T_n\) as the time when they are observed, although the below definition doesn’t require either variable to have those particular meanings. We say that the stochastic process is a MRP iff the elapsed time between observations and the value of the observation depend only on the previous observation. Formally, we write:

\[P(T_{n+1} - T_{n} \leq t, X_{n+1} = j | (X_0, T_0), (X_1, T_1), ..., (X_n = i, T_n)) = P(T_{n+1} - T_{n} \leq t, X_{n+1} = j | X_n = i)\]This is akin to a Markov Process