Markov additive process

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

Finite or countable state space for J(t)
The process $$\{ (X(t), J(t)) : t\ge 0 \}$$ is a Markov additive process with continuous time parameter t if


 * 1) $$\{ (X(t), J(t)) ; t\ge 0 \}$$ 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.