Markov chains on a measurable state space

A Markov chain on a measurable state space is a discrete-time-homogeneous Markov chain with a measurable space as state space.

History
The definition of Markov chains has evolved during the 20th century. In 1953 the term Markov chain was used for stochastic processes with discrete or continuous index set, living on a countable or finite state space, see Doob. or Chung. Since the late 20th century it became more popular to consider a Markov chain as a stochastic process with discrete index set, living on a measurable state space.

Definition
Denote with $$(E, \Sigma)$$ a measurable space and with $$p$$ a Markov kernel with source and target $$(E , \Sigma)$$. A stochastic process $$(X_n)_{n \in \mathbb{N}}$$ on $$(\Omega,\mathcal{F},\mathbb{P})$$ is called a time homogeneous Markov chain with Markov kernel $$p$$ and start distribution $$\mu$$ if
 * $$ \mathbb{P}[X_0 \in A_0, X_1 \in A_1, \dots , X_n \in A_n] = \int_{A_0} \dots \int_{A_{n-1}} p(y_{n-1},A_n) \, p(y_{n-2},dy_{n-1}) \dots p(y_0,dy_1) \, \mu(dy_0)$$

is satisfied for any $$n \in \mathbb{N}, \, A_0,\dots,A_n \in \Sigma$$. One can construct for any Markov kernel and any probability measure an associated Markov chain.

Remark about Markov kernel integration
For any measure $$\mu \colon \Sigma \to [0,\infty] $$ we denote for $$\mu$$-integrable function $$f \colon E \to \mathbb{R}\cup\{ \infty, - \infty \}$$ the Lebesgue integral as $$ \int_E f(x) \, \mu(dx) $$. For the measure $$ \nu_x \colon \Sigma \to [0,\infty]$$ defined by $$ \nu_x(A):= p(x,A)$$ we used the following notation:
 * $$\int_E f(y) \, p(x,dy) :=\int_E f(y) \, \nu_x (dy).$$

Starting in a single point
If $$\mu$$ is a Dirac measure in $$x$$, we denote for a Markov kernel $$p$$ with starting distribution $$\mu$$ the associated Markov chain as $$(X_n)_{n \in \mathbb{N}}$$ on $$(\Omega,\mathcal{F},\mathbb{P}_x)$$ and the expectation value
 * $$ \mathbb{E}_x[X] = \int_\Omega X(\omega) \, \mathbb{P}_x(d\omega)$$

for a $$\mathbb{P}_x$$-integrable function $$X$$. By definition, we have then $$\mathbb{P}_x[X_0 = x] = 1 $$.

We have for any measurable function $$f \colon E \to [0,\infty]$$ the following relation:
 * $$\int_E f(y) \, p(x,dy) = \mathbb{E}_x[f(X_1)].$$

Family of Markov kernels
For a Markov kernel $$p$$ with starting distribution $$\mu$$ one can introduce a family of Markov kernels $$(p_n)_{n\in\mathbb{N}}$$ by
 * $$p_{n+1}(x,A) := \int_E p_n(y,A) \, p(x,dy)$$

for $$n \in \mathbb{N}, \, n \geq 1$$ and $$p_1 := p$$. For the associated Markov chain $$(X_n)_{n \in \mathbb{N}}$$ according to $$p$$ and $$\mu$$ one obtains
 * $$\mathbb{P}[X_0 \in A, \, X_n \in B ] = \int_A p_n(x,B) \, \mu(dx)$$.

Stationary measure
A probability measure $$\mu$$ is called stationary measure of a Markov kernel $$p$$ if
 * $$\int_A \mu(dx) = \int_E p(x,A) \, \mu(dx)$$

holds for any $$A \in \Sigma$$. If $$(X_n)_{n \in \mathbb{N}}$$ on $$(\Omega,\mathcal{F},\mathbb{P})$$ denotes the Markov chain according to a Markov kernel $$p$$ with stationary measure $$\mu$$, and the distribution of $$ X_0 $$ is $$ \mu $$, then all $$X_n$$ have the same probability distribution, namely:
 * $$ \mathbb{P}[X_n \in A ] = \mu(A) $$

for any $$A \in \Sigma$$.

Reversibility
A Markov kernel $$p$$ is called reversible according to a probability measure $$\mu$$ if
 * $$ \int_A p(x,B) \, \mu(dx) = \int_B p(x,A) \, \mu(dx) $$

holds for any $$A,B \in \Sigma$$. Replacing $$A=E$$ shows that if $$p$$ is reversible according to $$\mu$$, then $$\mu$$ must be a stationary measure of $$p$$.