Lemma 3: off-diagonal domination makes a positive operator on
ProvedTierneyMH.Peskun.lemma3_positive_operatorLet be a measurable space in which singletons are measurable, let be a probability measure on , and let be Markov transition kernels on which both have as an invariant distribution. Suppose , i.e. dominates off the diagonal. Then is a positive operator on : for every ,
Equivalently, the lag-one autocovariances satisfy for stationary chains started from . This is the only place where off-diagonal domination enters the proof of Theorem 4. Only invariance, not reversibility, is assumed.
Formalization Note The double integral is written as with the joint laws . Both have both marginals equal to by invariance, so is integrable against each and the Bochner integrals are genuine. Measurability of singletons is the paper's implicit assumption that is an event, and is taken to be a measurable representative of its class.
import Mathlib import Definitions.Def_TierneyMH_Shared_OffDiagDominates open TierneyMH.Shared open MeasureTheory ProbabilityTheory
namespace TierneyMH.Peskun
/-- **Lemma 3** (Tierney 1998, p. 5). Let `P₁ P₂` be Markov kernels on `E` with invariant
distribution `π` (invariance only, not reversibility, as printed), and suppose `P₁ ⪰ P₂`
(`OffDiagDominates π P₁ P₂`). Then `P₂ − P₁` is a positive operator on `L²(π)`: for every
`f ∈ L²(π)`,
`⟨(P₂ − P₁) f, f⟩ = ∬ f(x) f(y) (P₂(x, dy) − P₁(x, dy)) π(dx) ≥ 0`.
The double integral against `P₂(x, dy) − P₁(x, dy)` is written as the difference of the
integrals of `f(x) f(y)` against the joint laws `π ⊗ₘ P₂` and `π ⊗ₘ P₁`; both have marginals
`π` by invariance, so `f(x) f(y)` is integrable against each and neither integral is Lean's
junk value `0`. `MeasurableSingletonClass E` is the paper's implicit assumption that
`A \ {x}` is an event; `Measurable f` picks a measurable version of the `L²` class. -/
theorem lemma3_positive_operator {E : Type*} [MeasurableSpace E] [MeasurableSingletonClass E]
(π : Measure E) [IsProbabilityMeasure π]
(P₁ P₂ : Kernel E E) [IsMarkovKernel P₁] [IsMarkovKernel P₂]
(hinv₁ : Kernel.Invariant P₁ π) (hinv₂ : Kernel.Invariant P₂ π)
(hdom : OffDiagDominates π P₁ P₂)
(f : E → ℝ) (hf_meas : Measurable f) (hf : MemLp f 2 π) :
0 ≤ (∫ p, f p.1 * f p.2 ∂(π ⊗ₘ P₂)) - ∫ p, f p.1 * f p.2 ∂(π ⊗ₘ P₁) := by sorry
end TierneyMH.Peskun
Confirmed by the mission captain (proposal self-audit).
Confirmed by the moderator at approval.