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Proposition 1.14 -- existence of a positive stationary distribution

Open
MarkovMixing.exists_stationary_pos

by Shuze Chen · Aug 21, 2026 · Mathlib c5ea003 (Lean v4.30.0)

markov-chainsmixing-timesprobability

An irreducible chain on a finite nonempty state space has a stationary distribution π\piπ with π(x)>0\pi(x)>0π(x)>0 for every state xxx, satisfying moreover

π(x) Ex(τx+)=1,\pi(x)\,\mathbb{E}_x(\tau_x^+)=1,π(x)Ex​(τx+​)=1,

i.e. π(x)=1/Ex(τx+)\pi(x)=1/\mathbb{E}_x(\tau_x^+)π(x)=1/Ex​(τx+​) where τx+\tau_x^+τx+​ is the first return time to xxx. The identity is stated multiplicatively, so a divergent return-time series (which the encoding would send to the junk value 000) cannot satisfy it vacuously.

Preamble
import Definitions.Def_mm_path
Formal statement
namespace MarkovMixing

/-- **Proposition 1.14** (LPW): an irreducible chain has a stationary
distribution `π` with `π(x) > 0` for all `x`, and moreover
`π(x) = 1 / E_x(τ⁺_x)` — stated multiplicatively as
`π(x) · E_x(τ⁺_x) = 1`. -/
theorem exists_stationary_pos {V : Type*} [Fintype V] [DecidableEq V] [Nonempty V]
    (P : Matrix V V ℝ) (hP : IsStochastic P) (hirr : Irreducible P) :
    ∃ π : V → ℝ, IsStationary P π ∧ (∀ x : V, 0 < π x) ∧
      ∀ x : V, π x * expReturnTime P x = 1 := by
  sorry

end MarkovMixing
Source
D. A. Levin, Y. Peres, E. L. Wilmer, Markov Chains and Mixing Times, AMS 2009, https://documents.epfl.ch/groups/i/ip/ipg/www/2013-2014/Random_Walks/markovmixing.pdf, Section 1.5.3, Proposition 1.14, pp. 12-13
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What the Lean code literally says, in plain math · claude-fable-5

Let VVV be an arbitrary nonempty finite type with decidable equality, and let PPP be a V×VV \times VV×V matrix of real numbers. Assume two hypotheses: (i) PPP is stochastic in the sense that every entry satisfies P(x,y)≥0P(x,y) \ge 0P(x,y)≥0 and every row sums to one, ∑yP(x,y)=1\sum_y P(x,y) = 1∑y​P(x,y)=1; and (ii) PPP is irreducible in the sense that for every ordered pair x,y∈Vx, y \in Vx,y∈V there exists a natural number t≥0t \ge 0t≥0 with (Pt)(x,y)>0(P^t)(x,y) > 0(Pt)(x,y)>0 — note that t=0t = 0t=0 is allowed, and since P0P^0P0 is the identity matrix, the condition is automatically satisfied when x=yx = yx=y, so this hypothesis only constrains pairs with x≠yx \neq yx=y and in particular says nothing about returns to a state. The theorem then asserts the existence of a function π:V→R\pi : V \to \mathbb{R}π:V→R satisfying three conjuncts simultaneously. First, π\piπ is stationary for PPP: this means both that π\piπ is a probability distribution (π(x)≥0\pi(x) \ge 0π(x)≥0 for all xxx, and ∑xπ(x)=1\sum_x \pi(x) = 1∑x​π(x)=1) and that the row-vector–matrix product fixes π\piπ, i.e. ∑xπ(x) P(x,y)=π(y)\sum_x \pi(x)\,P(x,y) = \pi(y)∑x​π(x)P(x,y)=π(y) for every y∈Vy \in Vy∈V. Second, π\piπ is strictly positive: π(x)>0\pi(x) > 0π(x)>0 for every x∈Vx \in Vx∈V. Third, for every x∈Vx \in Vx∈V,

π(x)⋅m(x)=1,\pi(x)\cdot m(x) = 1,π(x)⋅m(x)=1,

where m(x)m(x)m(x) (written expReturnTime P x in the source) is the following explicitly constructed real number. For each t≥0t \ge 0t≥0, define the avoidance mass

at(x)  =  ∑ω∏i=0t−1P(ωi,ωi+1),a_t(x) \;=\; \sum_{\omega} \prod_{i=0}^{t-1} P\bigl(\omega_i, \omega_{i+1}\bigr),at​(x)=ω∑​i=0∏t−1​P(ωi​,ωi+1​),

where the sum runs over all sequences ω=(ω0,ω1,…,ωt)∈Vt+1\omega = (\omega_0, \omega_1, \dots, \omega_t) \in V^{t+1}ω=(ω0​,ω1​,…,ωt​)∈Vt+1 such that ω0=x\omega_0 = xω0​=x and ωi≠x\omega_i \neq xωi​=x for every index 1≤i≤t1 \le i \le t1≤i≤t (an empty product for t=0t = 0t=0, so a0(x)=1a_0(x) = 1a0​(x)=1); probabilistically, at(x)a_t(x)at​(x) is the chance that a chain started at xxx has not yet returned to xxx by time ttt. Then m(x)=∑t=0∞at(x)m(x) = \sum_{t=0}^{\infty} a_t(x)m(x)=∑t=0∞​at​(x), taken as a topological sum of real numbers — with the convention that if the series fails to converge, this sum is defined to be 000. By the tail-sum formula, m(x)m(x)m(x) is (when the series converges) the expected first return time to xxx, so the third conjunct asserts π(x)=1/m(x)\pi(x) = 1/m(x)π(x)=1/m(x) for every state, in the multiplied form π(x) m(x)=1\pi(x)\, m(x) = 1π(x)m(x)=1; in particular it forces each m(x)m(x)m(x) to be finite and nonzero as encoded (a divergent series, giving m(x)=0m(x)=0m(x)=0, would make the equation π(x)⋅0=1\pi(x)\cdot 0 = 1π(x)⋅0=1 unsatisfiable). The statement claims mere existence (∃\exists∃, not unique existence) of such a π\piπ.

Human review
  • Endorsed by Community (Bot) · Aug 21, 2026

  • Endorsed by Shuze Chen · Aug 21, 2026

    Confirmed by the mission captain (proposal self-audit).

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