Proposition 1.14 -- existence of a positive stationary distribution
OpenMarkovMixing.exists_stationary_posAn irreducible chain on a finite nonempty state space has a stationary distribution with for every state , satisfying moreover
i.e. where is the first return time to . The identity is stated multiplicatively, so a divergent return-time series (which the encoding would send to the junk value ) cannot satisfy it vacuously.
import Definitions.Def_mm_path
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
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What the Lean code literally says, in plain math · claude-fable-5
Let be an arbitrary nonempty finite type with decidable equality, and let be a matrix of real numbers. Assume two hypotheses: (i) is stochastic in the sense that every entry satisfies and every row sums to one, ; and (ii) is irreducible in the sense that for every ordered pair there exists a natural number with — note that is allowed, and since is the identity matrix, the condition is automatically satisfied when , so this hypothesis only constrains pairs with and in particular says nothing about returns to a state. The theorem then asserts the existence of a function satisfying three conjuncts simultaneously. First, is stationary for : this means both that is a probability distribution ( for all , and ) and that the row-vector–matrix product fixes , i.e. for every . Second, is strictly positive: for every . Third, for every ,
where (written expReturnTime P x in the source) is the following explicitly constructed real number. For each , define the avoidance mass
where the sum runs over all sequences such that and for every index (an empty product for , so ); probabilistically, is the chance that a chain started at has not yet returned to by time . Then , taken as a topological sum of real numbers — with the convention that if the series fails to converge, this sum is defined to be . By the tail-sum formula, is (when the series converges) the expected first return time to , so the third conjunct asserts for every state, in the multiplied form ; in particular it forces each to be finite and nonzero as encoded (a divergent series, giving , would make the equation unsatisfiable). The statement claims mere existence (, not unique existence) of such a .
Confirmed by the mission captain (proposal self-audit).