Harris chain


In the mathematical study of stochastic processes, a Harris chain is a Markov chain where the chain returns to a particular part of the state space an unbounded number of times. Harris chains are regenerative processes and are named after Theodore Harris. The theory of Harris chains and Harris recurrence is useful for treating Markov chains on general state spaces.

Definition

Let be a Markov chain on a general state space Ω with stochastic kernel K. The kernel represents a generalized one-step transition probability law, so that P = K for all states x in Ω and all measurable sets C ⊆ Ω. The chain is a Harris chain if there exists A ⊆ Ω, ϵ > 0, and probability measure ρ with ρ = 1 such that
  1. If τA := inf, then P = 1 for all x ∈ Ω.
  2. If x ∈ A and C ⊆ Ω, then K ≥ ερ.
The first part of the definition ensures that the chain returns to some state within A with probability 1, regardless of where it starts. It follows that it visits state A infinitely often. The second part implies that once the Markov chain is in state A, its next-state can be generated with the help of an independent Bernoulli coin flip. To see this, first note that the parameter ε must be between 0 and 1. Now let x be a point in A and suppose Xn = x. To choose the next-state Xn+1, independently flip a biased coin with success probability ϵ. If the coin flip is successful, choose a next-state Xn+1 ∈ Ω according to the probability measure ρ. Else, choose a next-state Xn+1 according to the measure P = − ερ)/.
Two random processes and that have the same probability law and are Harris chains according to the above definition can be coupled as follows: Suppose that Xn=x and Yn = y, where x and y are points in A. Using the same coin flip to decide the next-state of both processes, it follows that the next states are the same with probability at least ε.

Examples

Example 1: Countable state space

Let Ω be a countable state space. The kernel K is defined by the one-step conditional transition probabilities P for x,y ∈ Ω. The measure ρ is a probability mass function on the states, so that ρ ≥ 0 for all x ∈ Ω, and the sum of the ρ probabilities is equal to one. Suppose the above definition is satisfied for
a given set A ⊆ Ω and a given parameter ε > 0. Then P ≥ ερ for all x ∈ A and all c ∈ Ω.

Example 2: Chains with continuous densities

Let, Xn ∈ Rd be a Markov chain with a kernel that is absolutely continuous with respect to Lebesgue measure:
such that K is a continuous function.
Pick such that K > 0, and let A and Ω be open sets containing x0 and y0 respectively that are sufficiently small so that K ≥ ε > 0 on A × Ω. Letting ρ = |Ω ∩ C|/|Ω| where |Ω| is the Lebesgue measure of Ω, we have that in the above definition holds. If holds, then is a Harris chain.

Reducibility and periodicity

In the following, R := inf ; i.e. R is the first time after time 0 that the process enters region A.
Definition: If for all L, P = 1, then the Harris chain is called recurrent.
Definition: A recurrent Harris chain Xn is aperiodic if ∃N, such that ∀n ≥ N, ∀L, P > 0.
Theorem: Let Xn be an aperiodic recurrent Harris chain with stationary distribution π. If P =1 then as n → ∞, distTV → 0.