# Markov renewal process

In probability and statistics a Markov renewal process (MRP) is a random process that generalizes the notion of Markov jump processes. Other random processes like Markov chains, Poisson processes and renewal processes can be derived as special cases of MRP's.

## Definition

An illustration of a Markov renewal process

Consider a state space ${\displaystyle \mathrm {S} .}$  Consider a set of random variables ${\displaystyle (X_{n},T_{n})}$ , where ${\displaystyle T_{n}}$  are the jump times and ${\displaystyle X_{n}}$  are the associated states in the Markov chain (see Figure). Let the inter-arrival time, ${\displaystyle \tau _{n}=T_{n}-T_{n-1}}$ . Then the sequence ${\displaystyle (X_{n},T_{n})}$  is called a Markov renewal process if

{\displaystyle {\begin{aligned}&\Pr(\tau _{n+1}\leq t,X_{n+1}=j\mid (X_{0},T_{0}),(X_{1},T_{1}),\ldots ,(X_{n}=i,T_{n}))\\[5pt]={}&\Pr(\tau _{n+1}\leq t,X_{n+1}=j\mid X_{n}=i)\,\forall n\geq 1,t\geq 0,i,j\in \mathrm {S} \end{aligned}}}

## Relation to other stochastic processes

1. If we define a new stochastic process ${\displaystyle Y_{t}:=X_{n}}$  for ${\displaystyle t\in [T_{n},T_{n+1})}$ , then the process ${\displaystyle Y_{t}}$  is called a semi-Markov process. Note the main difference between an MRP and a semi-Markov process is that the former is defined as a two-tuple of states and times, whereas the latter is the actual random process that evolves over time and any realisation of the process has a defined state for any given time. The entire process is not Markovian, i.e., memoryless, as happens in a continuous time Markov chain/process (CTMC). Instead the process is Markovian only at the specified jump instants. This is the rationale behind the name, Semi-Markov.[1][2][3] (See also: hidden semi-Markov model.)
2. A semi-Markov process (defined in the above bullet point) where all the holding times are exponentially distributed is called a CTMC. In other words, if the inter-arrival times are exponentially distributed and if the waiting time in a state and the next state reached are independent, we have a CTMC.
{\displaystyle {\begin{aligned}&\Pr(\tau _{n+1}\leq t,X_{n+1}=j\mid (X_{0},T_{0}),(X_{1},T_{1}),\ldots ,(X_{n}=i,T_{n}))\\[3pt]={}&\Pr(\tau _{n+1}\leq t,X_{n+1}=j\mid X_{n}=i)\\[3pt]={}&\Pr(X_{n+1}=j\mid X_{n}=i)(1-e^{-\lambda _{i}t}),{\text{ for all }}n\geq 1,t\geq 0,i,j\in \mathrm {S} ,i\neq j\end{aligned}}}
3. The sequence ${\displaystyle X_{n}}$  in the MRP is a discrete-time Markov chain. In other words, if the time variables are ignored in the MRP equation, we end up with a DTMC.
${\displaystyle \Pr(X_{n+1}=j\mid X_{0},X_{1},\ldots ,X_{n}=i)=\Pr(X_{n+1}=j\mid X_{n}=i)\,\forall n\geq 1,i,j\in \mathrm {S} }$
4. If the sequence of ${\displaystyle \tau }$ s are independent and identically distributed, and if their distribution does not depend on the state ${\displaystyle X_{n}}$ , then the process is a renewal process. So, if the states are ignored and we have a chain of iid times, then we have a renewal process.
${\displaystyle \Pr(\tau _{n+1}\leq t\mid T_{0},T_{1},\ldots ,T_{n})=\Pr(\tau _{n+1}\leq t)\,\forall n\geq 1,\forall t\geq 0}$