Markov chains: stationary distribution
Irreducible
Definition
A transition matrix P is called irreducible, if for any x, y $\in \Omega$, there exists a number n (possibly depending on x,y) such that $P^n(x,y) > 0$.
For any x $\in \Omega, define T(x) = {n \geq 1: P^n(x,x) > 0}$. The period of state x is the greatest common divisor of T(x), denoted by gcd(T(x)).
Lemma
if P is irreducible, then gcd(T(x)) = gcd(T(y)) for all x, y $\in \Omega$.
Aperiodic
Definition
For an irreducible chain, the period of the chain is defined to be the period which is common to all states. the chain is aperiodic if all states have period 1.
Example: Consider a simple random walk on N-cycle where N is odd. Then the walk is irreducible and aperiodic.
Theorem
If P is irreducible and aperiodic, then there exists an integer r such that
$P^n(x,y) > 0, \forall x, y \in \Omega, \forall n \geq r$.
Existence of stationary distribution
Definition
For x $\in \Omega$, define
$\tau_x = $ inf{$n \geq 0: X_n = x$}, $\tau_x^+ =$ inf{n $ \geq 1: X_n = x$}.
$\tau_x : the hitting time for x. \tau_x^+ : the first return time when X_0 = x$.
Lemma
Suppose that P is irreducible. Then, for any x, y $\in \Omega$, we have $\mathbb{E}_x[\tau_y^+] < \infty$
Theorem
Suppose that P is irreducible, then there exists a probability measure $\pi$ such that $\pi = \pi P$ and $\pi(x)$ > 0 $\forall x \in \Omega$.
Uniqueness of stationary distribution
Lemma
Suppose that P is irreducible. Then any harmonic function f on $\Omega$ has to be constant.
Theorem
Suppose that Pis irreducible. Then there exists a unique stationary distribution. Moreover,
$\pi(x) = \frac{a}{\mathbb{E}_x[\tau_x^+]}, \forall x \in \Omega$.
Summary
In the proof of the existence and the uniquencess of stationary distribution, the crucial assumption is that P is irreducible. When P is irreducible, we can explicitly write out the stationary distribution
$\pi(x) = \frac{1}{\mathbb{E}_x[\tau_x^+]} > 0, \forall x \in \Omega$.
In fact, the existence does not require irreducibility, but the uniqueness does.
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