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Time-homogeneity at an arbitrary offset: the kkk-shift commutes with one-step extension

Proved
MarkovChainCLT.partialTraj_map_shiftK_succ

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

ionescu-tulceamarkov-chainmixingpath-spaceprobability

Let PPP be a Markov kernel on X\mathsf{X}X, and write σk\sigma^kσk for the shift that drops the first kkk coordinates of a partial trajectory, (σkv)i=vk+i(\sigma^k v)_i = v_{k+i}(σkv)i​=vk+i​. Then for all k,mk, mk,m and every partial trajectory v=(v0,…,vk+m)v = (v_0,\dots,v_{k+m})v=(v0​,…,vk+m​),

σ∗k[partialTraj(k+m, k+m+1)(v)]  =  partialTraj(m, m+1)(σkv).\sigma^k_*\bigl[\mathrm{partialTraj}(k{+}m,\,k{+}m{+}1)(v)\bigr] \;=\; \mathrm{partialTraj}(m,\,m{+}1)\bigl(\sigma^k v\bigr).σ∗k​[partialTraj(k+m,k+m+1)(v)]=partialTraj(m,m+1)(σkv).

What it says. Extending a trajectory by one step and then dropping its first kkk coordinates is the same as dropping the first kkk coordinates and then extending. This is the arbitrary-offset form of time-homogeneity; the case k=1k = 1k=1 is the basic statement that the shift commutes with one-step extension.

Why the offset version is needed separately. The k=1k=1k=1 case suffices to prove that the law of the whole trajectory started at a point is shift-covariant, and hence that a chain started from an invariant measure is stationary. It does not suffice for the conditional statements that quantitative mixing estimates require: bounding the dependence between σ(X0,…,Xj)\sigma(X_0,\dots,X_j)σ(X0​,…,Xj​) and σ(Xj+n,Xj+n+1,… )\sigma(X_{j+n}, X_{j+n+1},\dots)σ(Xj+n​,Xj+n+1​,…) means comparing a trajectory conditioned on its first j+1j{+}1j+1 coordinates with a chain restarted nnn steps later, and that comparison is indexed by two independent offsets. Iterating the k=1k=1k=1 lemma cannot be done painlessly either, because the natural composition identity σk+1=σk∘σ\sigma^{k+1} = \sigma^k \circ \sigmaσk+1=σk∘σ forces the index arithmetic (m+1)+k(m{+}1)+k(m+1)+k versus (m+k)+1(m{+}k)+1(m+k)+1, which is not definitional in N\mathbb{N}N — addition recurses on its second argument. Writing the total length as k+mk+mk+m from the outset, as here, makes k+(m+1)k+(m{+}1)k+(m+1) and (k+m)+1(k{+}m)+1(k+m)+1 definitionally equal and the induction goes through without transport.

Two facts consumed, exactly as in the unit-offset case. The left side extends using the kernel P(vk+m,⋅)P(v_{k+m},\cdot)P(vk+m​,⋅) and the right side using P((σkv)m,⋅)P\bigl((\sigma^k v)_m,\cdot\bigr)P((σkv)m​,⋅); these agree because (σkv)m=vk+m(\sigma^k v)_m = v_{k+m}(σkv)m​=vk+m​ — the shift preserves the last coordinate, which is all the transition law depends on (the Markov property) — and because the kernel is the same PPP at both times (time-homogeneity). For a genuinely time-inhomogeneous family the statement is false.

Proof. Rewrite both sides with the explicit pushforward description of a one-step extension, so each is a pushforward of the single measure P(vk+m,⋅)P(v_{k+m},\cdot)P(vk+m​,⋅) along a gluing map. After identifying the kernels, the claim reduces to a pointwise identity between the two gluing maps, settled by splitting on whether the index iii satisfies i≤mi \le mi≤m: the two branch conditions k+i≤k+mk+i \le k+mk+i≤k+m and i≤mi \le mi≤m are equivalent by cancellation, the first branch returns vk+iv_{k+i}vk+i​ on both sides, and the second returns the newly drawn coordinate on both sides.

Preamble
import Definitions.Def_MarkovChainPathMeasure

open Filter Finset Function MeasurableEquiv MeasurableSpace MeasureTheory Preorder ProbabilityTheory
open scoped ENNReal NNReal Topology
Formal statement
theorem MarkovChainCLT.partialTraj_map_shiftK_succ {S : Type*} [MeasurableSpace S]
    (P : Kernel S S) [IsMarkovKernel P] (k m : ℕ) (v : Π _i : Finset.Iic (k + m), S) :
    (Kernel.partialTraj (X := fun _ : ℕ => S) (BanditAlgorithm.markovChainStep P)
        (k + m) (k + m + 1) v).map
        (fun u i => u ⟨k + i.1,
          Finset.mem_Iic.2 (Nat.add_le_add_left (Finset.mem_Iic.mp i.2) k)⟩)
      = Kernel.partialTraj (X := fun _ : ℕ => S) (BanditAlgorithm.markovChainStep P) m (m + 1)
          (fun i => v ⟨k + i.1,
            Finset.mem_Iic.2 (Nat.add_le_add_left (Finset.mem_Iic.mp i.2) k)⟩) := by sorry
Source
C. T. Ionescu Tulcea, "Mesures dans les espaces produits", Atti Accad. Naz. Lincei Rend. 7 (1949) 208-211; S. P. Meyn and R. L. Tweedie, Markov Chains and Stochastic Stability, 2nd ed., Cambridge 2009, Ch. 3; C. J. Geyer, "Practical Markov Chain Monte Carlo", Statistical Science 7 (1992) 473-483, Section 3.

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