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The past σ\sigmaσ-algebra is a single pullback along the restriction

Proved
MarkovChainCLT.processSigma_Iic_eq_comap_restrict

by LukeBernese · Aug 15, 2026 · Mathlib c5ea003 (Lean v4.30.0)

measure-theorymixingpath-spaceprobability

For the coordinate process Yi(ω)=ωiY_i(\omega) = \omega_iYi​(ω)=ωi​ on path space, the σ\sigmaσ-algebra generated by the coordinates up to time kkk coincides with the pullback of the product σ\sigmaσ-algebra on ∏i≤kX\prod_{i \le k}\mathsf{X}∏i≤k​X along the restriction map ω↦(ωi)i≤k\omega \mapsto (\omega_i)_{i \le k}ω↦(ωi​)i≤k​:

σ(Y0,…,Yk)  =  (product σ-algebra on ∏i≤kX)∘frk−1.\sigma\bigl(Y_0,\dots,Y_k\bigr) \;=\; \bigl(\text{product }\sigma\text{-algebra on } \textstyle\prod_{i\le k}\mathsf{X}\bigr) \circ \mathrm{fr}_k^{-1}.σ(Y0​,…,Yk​)=(product σ-algebra on ∏i≤k​X)∘frk−1​.

Companion to the future-σ\sigmaσ-algebra identification. Mixing coefficients compare the past σ(Y0,…,Yk)\sigma(Y_0,\dots,Y_k)σ(Y0​,…,Yk​) with the future σ(Yi:i≥k+n)\sigma(Y_i : i \ge k+n)σ(Yi​:i≥k+n); both appear in their definitions as suprema of single-coordinate pullbacks. This theorem handles the past exactly as its companion handles the future: every past event is {ω:frkω∈A0}\{\omega : \mathrm{fr}_k\omega \in A_0\}{ω:frk​ω∈A0​} for a single measurable A0⊆∏i≤kXA_0 \subseteq \prod_{i\le k}\mathsf{X}A0​⊆∏i≤k​X.

This is what allows the Ionescu–Tulcea decomposition traj0=trajk∘partialTraj(0,k)\mathrm{traj}_0 = \mathrm{traj}_k \circ \mathrm{partialTraj}(0,k)traj0​=trajk​∘partialTraj(0,k) to be applied: that decomposition conditions on the first k+1k+1k+1 coordinates, and the statement that a past event is determined by those coordinates has to be available in the form "AAA is a pullback along frk\mathrm{fr}_kfrk​", not as membership in an abstract supremum. Together with the future identification, a mixing estimate becomes a genuine computation: condition on frk\mathrm{fr}_kfrk​, use the restart property on the shifted future, and compare with stationarity.

Proof. The product σ\sigmaσ-algebra on ∏i≤kX\prod_{i \le k}\mathsf{X}∏i≤k​X is the supremum of the pullbacks along its own coordinate evaluations; pullback commutes with suprema and composition, so the right-hand side is the supremum over i∈Iic ki \in \mathrm{Iic}\,ki∈Iick of the pullbacks along ω↦ωi\omega \mapsto \omega_iω↦ωi​. Matching this with ⨆i≤k\bigsqcup_{i \le k}⨆i≤k​ is the translation between the subtype of the finite set Iic k\mathrm{Iic}\,kIick and the condition i≤ki \le ki≤k, in both directions.

Preamble
import Definitions.Def_MixingCoefficients

open MeasureTheory MeasurableSpace
open MarkovChainCLT
Formal statement
theorem MarkovChainCLT.processSigma_Iic_eq_comap_restrict {X : Type*} [MeasurableSpace X] (k : ℕ) :
    processSigma (fun i (ω : ℕ → X) => ω i) (Set.Iic k)
      = MeasurableSpace.comap
          (fun ω : ℕ → X => fun i : Finset.Iic k => ω i.1) inferInstance := by sorry
Source
P. Billingsley, Probability and Measure, 3rd ed., Wiley 1995, Section 36; O. Kallenberg, Foundations of Modern Probability, 2nd ed., Springer 2002, Ch. 1 and Ch. 8; G. L. Jones, "On the Markov Chain Central Limit Theorem", Probability Surveys 1 (2004) 299-320, Section 3 (Definitions 1-3).

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