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Milds In applied probability, a Markov additive process (MAP) is a bivariate Markov process where the future states depends only on one of the variables.

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## Definition

### Finite or countable state space for J(t)

The process $\{(X(t),J(t)):t\geq 0\}$ is a Markov additive process with continuous time parameter t if

1. $\{(X(t),J(t));t\geq 0\}$ is a Markov process
2. the conditional distribution of $(X(t+s)-X(t),J(t+s))$ given $(X(t),J(t))$ depends only on $J(t)$ .

The state space of the process is R × S where X(t) takes real values and J(t) takes values in some countable set S.

### General state space for J(t)

For the case where J(t) takes a more general state space the evolution of X(t) is governed by J(t) in the sense that for any f and g we require

$\mathbb {E} [f(X_{t+s}-X_{t})g(J_{t+s})|{\mathcal {F}}_{t}]=\mathbb {E} _{J_{t},0}[f(X_{s})g(J_{s})]$ .

## Example

A fluid queue is a Markov additive process where J(t) is a continuous-time Markov chain.

## Applications

Çinlar uses the unique structure of the MAP to prove that, given a gamma process with a shape parameter that is a function of Brownian motion, the resulting lifetime is distributed according to the Weibull distribution.

Kharoufeh presents a compact transform expression for the failure distribution for wear processes of a component degrading according to a Markovian environment inducing state-dependent continuous linear wear by using the properties of a MAP and assuming the wear process to be temporally homogeneous and that the environmental process has a finite state space.

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