Definition (Joint Markov Chain)
Let S be a state space, and P a transition matrix over S. Consider two independent MCs {Xi}i=0∞∼\mboxMarkov(λ,P) {Yi}i=0∞∼\mboxMarkov(μ,P)For each n≥0 we define Wn=(Xn,Yn)
Lemma (Wn∼\mboxMarkov(θ~,P~))
{Wi}i=0∞ is a MC with the state space S2, initial distribution θ~ and transition matrix P~: θ~(i,j)=λiμjP~(i,j),(i′,j′)=Pi,i′Pj,j′\mboxforanyi,j∈S\mboxforanyi,i′j,j′∈S
Lemma (Irreducibility of the JMC)
If P is irreducible and aperiodic, then P~ is irreducible
Lemma (Invariant Distribution of the JMC)
Let π be an Invariant Distribution for P. Define π~(i,j)=πiπj\mboxfori,j∈SThen π~ is invariant for P~.
Lemma (Coupling lemma)
Let T=inf{n≥0:Xn=Yn}. Define for each n≥0 \begin{align*}
{X_{n}'=\left\{\begin{array}\ X_{n} & \mbox{if }n<T\\ Y_n&\mbox{if }n\ge T\end{array}\right.} \ \ \ \ \ {Y_{n}'=\left\{\begin{array}\ Y_{n} & \mbox{if }n<T\\ X_n&\mbox{if }n\ge T\end{array}\right.}
\end{align*} Let Wn′=(Xn′,Yn′) for n≥0
- {Wn′:n≥0} have the same distribution as JMC {Wn:n≥0}
- Consequently, for any integer n, j∈Ssup∣P(Xn=j)−P(Yn=j)∣≤P(T>n)