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Trajectories, return times, and hitting times of a finite chain

Definition
mm_path

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

markov-chainsmixing-timesprobability

This file develops the finite-horizon trajectory calculus of a finite chain, following Sections 1.5 and 2.1 of Levin-Peres-Wilmer. A trajectory of length ttt is a map ω:{0,…,t}→V\omega:\{0,\dots,t\}\to Vω:{0,…,t}→V, carrying the weight ∏i<tP(ωi,ωi+1)\prod_{i<t}P(\omega_i,\omega_{i+1})∏i<t​P(ωi​,ωi+1​); the probability of any event determined by the first ttt steps of the chain started at xxx is the finite sum of the weights of the trajectories with ω0=x\omega_0=xω0​=x realizing it. In this way the file defines the tail probabilities Px{τz+>t}\mathbb P_x\{\tau^+_z>t\}Px​{τz+​>t} of the first hitting time τz+=min⁡{t≥1:Xt=z}\tau^+_z=\min\{t\ge1:X_t=z\}τz+​=min{t≥1:Xt​=z} (the starting state is exempt from the avoidance constraint), the refined event Px{Xt=y, τz+>t}\mathbb P_x\{X_t=y,\ \tau^+_z>t\}Px​{Xt​=y, τz+​>t}, the tails Px{τS>t}\mathbb P_x\{\tau_S>t\}Px​{τS​>t} of the hitting time τS=min⁡{t≥0:Xt∈S}\tau_S=\min\{t\ge0:X_t\in S\}τS​=min{t≥0:Xt​∈S} of a set of states (here the starting state is not exempt), and the first-passage probabilities Px{τa=t, τb>t}\mathbb P_x\{\tau_a=t,\ \tau_b>t\}Px​{τa​=t, τb​>t}. On top of these, expectations of hitting times are defined by the tail-sum formula

E(Y)=∑t≥0P{Y>t},\mathbb E(Y)=\sum_{t\ge0}\mathbb P\{Y>t\},E(Y)=t≥0∑​P{Y>t},

as infinite series in ttt: the expected hitting time Ex(τz+)\mathbb E_x(\tau^+_z)Ex​(τz+​), the expected return time Ex(τx+)\mathbb E_x(\tau^+_x)Ex​(τx+​), and the expected hitting time Ex(τS)\mathbb E_x(\tau_S)Ex​(τS​) of a set; and the probability Px{τa<τb}\mathbb P_x\{\tau_a<\tau_b\}Px​{τa​<τb​} of visiting aaa strictly before bbb is defined as the series ∑t≥0Px{τa=t, τb>t}\sum_{t\ge0}\mathbb P_x\{\tau_a=t,\ \tau_b>t\}∑t≥0​Px​{τa​=t, τb​>t} of first-passage probabilities. All infinite series follow the convention that a non-summable family sums to 000.

Definition code
import Definitions.Def_mm_basic
import Mathlib.Analysis.SpecificLimits.Basic

/-!
Finite trajectories, return times and hitting times for a finite Markov chain,
following Levin–Peres–Wilmer, *Markov Chains and Mixing Times*, §1.5 and §2.1.

A length-`t` trajectory is a function `ω : Fin (t+1) → V` recording the states
at times `0, 1, …, t`.  Under the chain law started at `x`, the trajectory `ω`
with `ω 0 = x` has probability `∏ i, P (ω i) (ω (i+1))`; every event
depending on the first `t` steps is a finite sum of such products.  Tail
probabilities of hitting times are expressed this way, and expectations are
recovered as `E Y = ∑_{t ≥ 0} P{Y > t}` for a nonnegative integer variable `Y`
(the identity used throughout LPW, e.g. in the proof of Lemma 1.13).
-/

namespace MarkovMixing

noncomputable section

open scoped BigOperators

variable {V : Type*} [Fintype V] [DecidableEq V]

/-- The probability weight `∏_{i<t} P(ω_i, ω_{i+1})` of the length-`t`
trajectory `ω` (conditional on its starting state `ω 0`). -/
def pathWeight (P : Matrix V V ℝ) {t : ℕ} (ω : Fin (t + 1) → V) : ℝ :=
  ∏ i : Fin t, P (ω i.castSucc) (ω i.succ)

/-- `P_x{τ⁺_z > t, X_t = y}`: the chain started at `x` is at `y` at time `t`
without having visited `z` at any of the times `1, …, t` (LPW §1.5.3,
Eq. (1.19), where `τ⁺_z = min {t ≥ 1 : X_t = z}` is the first return time). -/
def avoidHitProb (P : Matrix V V ℝ) (x z y : V) (t : ℕ) : ℝ :=
  ∑ ω : Fin (t + 1) → V,
    if ω 0 = x ∧ (∀ i : Fin (t + 1), i ≠ 0 → ω i ≠ z) ∧ ω (Fin.last t) = y then
      pathWeight P ω
    else 0

/-- `P_x{τ⁺_z > t}`: the chain started at `x` does not visit `z` at any of the
times `1, …, t` (LPW §1.5.2). -/
def avoidTailProb (P : Matrix V V ℝ) (x z : V) (t : ℕ) : ℝ :=
  ∑ ω : Fin (t + 1) → V,
    if ω 0 = x ∧ (∀ i : Fin (t + 1), i ≠ 0 → ω i ≠ z) then pathWeight P ω else 0

/-- `E_x(τ⁺_z)`, the expected first time `≥ 1` at which the chain started at
`x` visits `z`, via the tail-sum formula `E Y = ∑_{t≥0} P{Y > t}`
(LPW §1.5.2–§1.5.3; junk value `0` when the tails are not summable). -/
def expHitTimePos (P : Matrix V V ℝ) (x z : V) : ℝ :=
  ∑' t : ℕ, avoidTailProb P x z t

/-- `E_x(τ⁺_x)`, the expected first return time to `x` (LPW §1.5.3). -/
def expReturnTime (P : Matrix V V ℝ) (x : V) : ℝ :=
  expHitTimePos P x x

/-- `P_x{τ_a = t, τ_b > t}`: started at `x`, the chain first visits `a` at
time `t` and has not visited `b` at any time `≤ t`.  (Here `τ_a` is the first
time `≥ 0` at which the chain is at `a`.) -/
def firstHitBeforeProb (P : Matrix V V ℝ) (x a b : V) (t : ℕ) : ℝ :=
  ∑ ω : Fin (t + 1) → V,
    if ω 0 = x ∧ ω (Fin.last t) = a ∧
        (∀ i : Fin (t + 1), i ≠ Fin.last t → ω i ≠ a) ∧ (∀ i : Fin (t + 1), ω i ≠ b) then
      pathWeight P ω
    else 0

/-- `P_x{τ_a < τ_b}`: started at `x`, the chain visits `a` (at some finite
time) strictly before it visits `b` (LPW §2.1). -/
def hitBeforeProb (P : Matrix V V ℝ) (x a b : V) : ℝ :=
  ∑' t : ℕ, firstHitBeforeProb P x a b t

/-- `P_x{τ_S > t}`: the chain started at `x` stays outside the set `S` up to
and including time `t`, where `τ_S = min {t ≥ 0 : X_t ∈ S}`. -/
def setAvoidTailProb (P : Matrix V V ℝ) (x : V) (S : Finset V) (t : ℕ) : ℝ :=
  ∑ ω : Fin (t + 1) → V,
    if ω 0 = x ∧ (∀ i : Fin (t + 1), ω i ∉ S) then pathWeight P ω else 0

/-- `E_x(τ_S)`, the expected first time `≥ 0` at which the chain started at `x`
is in `S`, via the tail-sum formula (LPW §2.1; junk value `0` when the tails
are not summable). -/
def expSetHitTime (P : Matrix V V ℝ) (x : V) (S : Finset V) : ℝ :=
  ∑' t : ℕ, setAvoidTailProb P x S t

end

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, Sections 1.5 and 2.1
Read-back

What the Lean code literally says, in plain math · claude-fable-5

Read-back: Def_mm_path

Throughout, VVV is an arbitrary finite type with decidable equality, and PPP is an arbitrary V×VV \times VV×V matrix of real numbers — no assumption is made anywhere in this file that PPP is stochastic, nonnegative, or otherwise probability-like. A "path of length ttt" below means a function ω:{0,1,…,t}→V\omega : \{0, 1, \dots, t\} \to Vω:{0,1,…,t}→V assigning a state to each of the t+1t+1t+1 time indices; the sums below range over all such functions.

pathWeight. Given the matrix PPP, a natural number ttt (implicit), and a path ω:{0,…,t}→V\omega : \{0,\dots,t\} \to Vω:{0,…,t}→V, the path weight is defined as the product of the matrix entries along the consecutive steps of the path:

wP(ω)=∏i=0t−1P(ω(i), ω(i+1)).w_P(\omega) = \prod_{i=0}^{t-1} P\big(\omega(i),\, \omega(i+1)\big).wP​(ω)=i=0∏t−1​P(ω(i),ω(i+1)).

When t=0t = 0t=0 (a path consisting of a single state), this is the empty product and equals 111, regardless of PPP or ω\omegaω. Since PPP is not assumed nonnegative, the weight may be any real number.

avoidHitProb. Given PPP, three states x,z,y∈Vx, z, y \in Vx,z,y∈V (not assumed distinct), and t∈Nt \in \mathbb{N}t∈N, this is the real number

∑ω:{0,…,t}→VwP(ω)⋅1[ ω(0)=x  ∧  (∀i∈{1,…,t}, ω(i)≠z)  ∧  ω(t)=y ],\sum_{\omega : \{0,\dots,t\} \to V} w_P(\omega) \cdot \mathbf{1}\Big[\,\omega(0) = x \;\wedge\; \big(\forall i \in \{1,\dots,t\},\ \omega(i) \neq z\big) \;\wedge\; \omega(t) = y\,\Big],ω:{0,…,t}→V∑​wP​(ω)⋅1[ω(0)=x∧(∀i∈{1,…,t}, ω(i)=z)∧ω(t)=y],

i.e. the sum of path weights over all length-ttt paths that start at xxx, end at yyy at time ttt, and avoid zzz at every time index except possibly time 000 (the starting state is exempt from the avoidance requirement, so x=zx = zx=z is allowed; but the final index ttt is not exempt when t≥1t \ge 1t≥1, so for t≥1t \ge 1t≥1 and y=zy = zy=z every summand vanishes). Degenerate case t=0t = 0t=0: the only time index is 000, the avoidance condition is vacuous, and the value is 111 if x=yx = yx=y and 000 otherwise (in particular it is 111 when x=y=zx = y = zx=y=z).

avoidTailProb. Given PPP, states x,z∈Vx, z \in Vx,z∈V, and t∈Nt \in \mathbb{N}t∈N, this is

∑ω:{0,…,t}→VwP(ω)⋅1[ ω(0)=x  ∧  (∀i∈{1,…,t}, ω(i)≠z) ],\sum_{\omega : \{0,\dots,t\} \to V} w_P(\omega) \cdot \mathbf{1}\Big[\,\omega(0) = x \;\wedge\; \big(\forall i \in \{1,\dots,t\},\ \omega(i) \neq z\big)\,\Big],ω:{0,…,t}→V∑​wP​(ω)⋅1[ω(0)=x∧(∀i∈{1,…,t}, ω(i)=z)],

the sum of weights of all length-ttt paths that start at xxx and avoid zzz at every time index from 111 through ttt inclusive; time 000 is again exempt, so x=zx = zx=z is permitted. No condition is placed on the endpoint. Degenerate case t=0t = 0t=0: the condition reduces to ω(0)=x\omega(0) = xω(0)=x, so the value is 111 (the weight of the single one-point path at xxx).

expHitTimePos. Given PPP and states x,z∈Vx, z \in Vx,z∈V, this is the value of the infinite series

∑t=0∞avoidTailProb(P,x,z,t),\sum_{t=0}^{\infty} \texttt{avoidTailProb}(P, x, z, t),t=0∑∞​avoidTailProb(P,x,z,t),

where the sum is a topological sum over N\mathbb{N}N: if the family (avoidTailProb(P,x,z,t))t≥0\big(\texttt{avoidTailProb}(P,x,z,t)\big)_{t \ge 0}(avoidTailProb(P,x,z,t))t≥0​ is not (unconditionally) summable, this expression is by convention equal to 000, not +∞+\infty+∞. Note the series starts at t=0t = 0t=0, and the t=0t = 0t=0 term always equals 111.

expReturnTime. Given PPP and a state x∈Vx \in Vx∈V, this is literally expHitTimePos(P,x,x)\texttt{expHitTimePos}(P, x, x)expHitTimePos(P,x,x): the (topological) sum over t≥0t \ge 0t≥0 of the total weight of length-ttt paths that start at xxx and do not visit xxx at any time in {1,…,t}\{1, \dots, t\}{1,…,t}. Again, if the series is not summable the value is 000 by convention.

firstHitBeforeProb. Given PPP, states x,a,b∈Vx, a, b \in Vx,a,b∈V (not assumed distinct), and t∈Nt \in \mathbb{N}t∈N, this is

∑ω:{0,…,t}→VwP(ω)⋅1[ ω(0)=x  ∧  ω(t)=a  ∧  (∀i≠t, ω(i)≠a)  ∧  (∀i∈{0,…,t}, ω(i)≠b) ],\sum_{\omega : \{0,\dots,t\} \to V} w_P(\omega) \cdot \mathbf{1}\Big[\,\omega(0) = x \;\wedge\; \omega(t) = a \;\wedge\; \big(\forall i \neq t,\ \omega(i) \neq a\big) \;\wedge\; \big(\forall i \in \{0,\dots,t\},\ \omega(i) \neq b\big)\,\Big],ω:{0,…,t}→V∑​wP​(ω)⋅1[ω(0)=x∧ω(t)=a∧(∀i=t, ω(i)=a)∧(∀i∈{0,…,t}, ω(i)=b)],

the sum of weights of all length-ttt paths that start at xxx, are at aaa at time ttt but at no earlier time (including time 000, so for t≥1t \ge 1t≥1 this forces x≠ax \neq ax=a), and never equal bbb at any time, including time 000 and time ttt. Consequences of the literal reading: if x=bx = bx=b every summand is 000 (the constraints ω(0)=x\omega(0) = xω(0)=x and ω(0)≠b\omega(0) \neq bω(0)=b conflict); if a=ba = ba=b every summand is 000 (the constraints ω(t)=a\omega(t) = aω(t)=a and ω(t)≠b\omega(t) \neq bω(t)=b conflict). Degenerate case t=0t = 0t=0: the "no earlier visit to aaa" condition is vacuous and the value is 111 if x=ax = ax=a and x≠bx \neq bx=b, else 000.

hitBeforeProb. Given PPP and states x,a,b∈Vx, a, b \in Vx,a,b∈V, this is the topological sum

∑t=0∞firstHitBeforeProb(P,x,a,b,t),\sum_{t=0}^{\infty} \texttt{firstHitBeforeProb}(P, x, a, b, t),t=0∑∞​firstHitBeforeProb(P,x,a,b,t),

with the same convention: the value is 000 if the family of terms is not summable.

setAvoidTailProb. Given PPP, a state x∈Vx \in Vx∈V, a finite subset S⊆VS \subseteq VS⊆V, and t∈Nt \in \mathbb{N}t∈N, this is

∑ω:{0,…,t}→VwP(ω)⋅1[ ω(0)=x  ∧  (∀i∈{0,…,t}, ω(i)∉S) ],\sum_{\omega : \{0,\dots,t\} \to V} w_P(\omega) \cdot \mathbf{1}\Big[\,\omega(0) = x \;\wedge\; \big(\forall i \in \{0,\dots,t\},\ \omega(i) \notin S\big)\,\Big],ω:{0,…,t}→V∑​wP​(ω)⋅1[ω(0)=x∧(∀i∈{0,…,t}, ω(i)∈/S)],

the sum of weights of all length-ttt paths that start at xxx and avoid the set SSS at every time index, including time 000 — unlike avoidTailProb, the starting state is not exempt here, so if x∈Sx \in Sx∈S the value is 000 for every ttt. SSS may be empty, in which case the avoidance condition is vacuous. Degenerate case t=0t = 0t=0: the value is 111 if x∉Sx \notin Sx∈/S and 000 if x∈Sx \in Sx∈S.

expSetHitTime. Given PPP, a state x∈Vx \in Vx∈V, and a finite subset S⊆VS \subseteq VS⊆V, this is the topological sum

∑t=0∞setAvoidTailProb(P,x,S,t),\sum_{t=0}^{\infty} \texttt{setAvoidTailProb}(P, x, S, t),t=0∑∞​setAvoidTailProb(P,x,S,t),

equal by convention to 000 whenever the family of terms is not summable. If x∈Sx \in Sx∈S, every term of the series is 000 and the sum is 000.

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  • 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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