Documentation

Mathlib.Probability.Kernel.Irreducible

Irreducibility of kernels #

A kernel κ : Kernel α α is φ-irreducible, for a given measure φ on α, if for every measurable set A with positive measure under φ, and for every a : α, there exists a positive integer n such that we have (κ ^ n) a A > 0.

When the kernel κ is the transition kernel of a Markov chain, this precisely means that the Markov chain is φ-irreducible, that is, there is a positive probability of reaching any (φ-positive measure) set of states from any other state within a finite number of steps.

Main definitions #

Main statements #

References #

A kernel κ : Kernel α α is φ-irreducible (w.r.t. a given measure φ on α), if for every measurable set A with positive measure under φ, and for every a : α, there exists an integer n such that (κ ^ n) a A > 0. Ref. Meyn-Tweedie Proposition 4.2.1(ii), page 89

  • irreducible A : Set α (hA : MeasurableSet A) (hφA : φ A > 0) (a : α) : ∃ (n : ), ((κ ^ n) a) A > 0
Instances
    theorem ProbabilityTheory.Kernel.isIrreducible_iff {α : Type u_1} { : MeasurableSpace α} (φ : MeasureTheory.Measure α) (κ : Kernel α α) :
    IsIrreducible φ κ ∀ ⦃A : Set α⦄, MeasurableSet Aφ A > 0∀ (a : α), ∃ (n : ), ((κ ^ n) a) A > 0
    theorem ProbabilityTheory.Kernel.isIrreducible_of_le_measure {α : Type u_1} { : MeasurableSpace α} {φ₁ φ₂ : MeasureTheory.Measure α} ( : φ₁ φ₂) {κ : Kernel α α} [ : IsIrreducible φ₂ κ] :
    IsIrreducible φ₁ κ