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One-dimensional simple symmetric walk is recurrent

Statement

Assume AC. Identify Z with Z1 and define, for A⊆Z, K(z,A):=12δz+1(A)+12δz−1(A). For each fixed z∈Z, use the canonical chain law Pz with initial measure δz and kernel K. This is the simple symmetric nearest-neighbor walk in dimension one (Simple symmetric walk on the integer lattice). With Tz+:=inf⁡{n≥1:Xn=z}, every state is recurrent: Pz(Tz+<∞)=1(z∈Z).

Facts & Assumptions

Given: AC, the state space Z with its full power-set sigma-algebra, the one-dimensional simple symmetric walk, and a fixed start z.

[A1]

AC is assumed by the canonical path-law and finite-dimensional-law suppliers used here. (The Axiom of Choice)

[F1]

In dimension one, the simple symmetric transition row has mass 1/2 at each of the two distinct neighbors z−1,z+1, and zero elsewhere. (Simple symmetric walk on the integer lattice)

[F2]

For each point w, δw is a probability measure. (A Dirac set function is a probability measure)

[F3]

A finite nonnegative weighted sum of measures is a measure. (Nonnegative scalar multiples and countable weighted sums of measures are measures)

[F4]

A probability kernel is a measure in its target variable for each source point, has total mass one, and is measurable in the source point for each measurable target set. (Measure kernel and probability kernel)

[F5]

A function is measurable when the preimage of each measurable target set is measurable in the source space. (A measurable function between measurable spaces)

[F6]

Under AC, the path space carries the canonical law for a specified probability initial measure and probability kernel. (Canonical Markov chain on path space)

[F7]

With initial measure δz, the fixed-start notation is Pz=Pδz and X0=z almost surely. (Initial distribution of a Markov chain)

[F8]

Under the finite-dimensional law, the probability of a finite cylinder is the iterated product of its initial and transition probabilities. (Finite-dimensional laws of a Markov chain)

[F9]

The matrix entries are p(z,w)=K(z,{w}) and p(n)(z,w)=Kn(z,{w}), with p(0)(z,w)=1{z=w}. (Transition matrices and n-step probabilities)

[F10]

(mk) is the number of k-element subsets of an m-element set. (The set [A]k of k-element subsets and the binomial coefficient (nk):=∣[n]k∣)

[F11]

For a fixed state, recurrence is equivalent to divergence of its return Green series: z is recurrent iff ∑n≥0p(n)(z,z)=∞. (Equivalent criteria for recurrence and transience)

[F12]

Recurrence means that the positive-time return probability is one. (Recurrent and transient states)

[F13]

Tz+=inf⁡{n≥1:Xn=z} is the strictly positive return time. (Hitting, return, and visit times)

[F14]

For n≥1, 4−n(2nn)∼(πn)−1/2. (The central binomial coefficient is asymptotic to 4^n divided by the square root of pi n)

[F15]

A set in bijection with N is countably infinite and hence at most countable. (Finite, countably infinite, countable, uncountable)

Proof

technique · count the finite return words, use the central-binomial asymptotic to make their Green series diverge, and apply the statewise recurrence criterion at each fixed start
1.1F15given

Define e:N→Z by e(0)=0, e(2k−1)=k, and e(2k)=−k for k≥1. Every integer occurs exactly once, so this is a bijection and Z is countably infinite, hence at most countable as required by the chain-law and recurrence suppliers.

1.2F1F2F3F4F5given

For each fixed z, the two Dirac measures in the displayed definition of K(z,⋅) are probability measures [F2]; their weighted sum is a measure by [F3], and its total mass is 1/2+1/2=1. For each fixed A⊆Z, the map z↦K(z,A) is measurable because every subset of the discrete source Z is measurable [F5]. Thus [F4] makes K a probability kernel. Its singleton entries agree with [F1], so this is exactly the d=1 simple symmetric walk kernel.

1.3A1F6F7F8F9given

Under AC [A1], [F6] supplies the canonical law with initial measure δz for each fixed z; [F7] names it Pz, and [F8] gives the probabilities of its finite path cylinders. The transition matrix of this chain is the matrix in [F9].

1.4F1F7F8F9F10given

Fix z∈Z and n≥0. A sign word (ε1,…,ε2n)∈{−1,1}2n determines the path Xj=z+∑i=1jεi for 0≤j≤2n. Each such cylinder has probability 2−2n by [F1], [F7], and [F8]. Distinct sign words give disjoint cylinders, and their endpoint equals z exactly when the word has equally many +1 and −1 entries. By [F10] there are (2nn) such words. Consequently p(2n)(z,z)=Pz(X2n=z)=4−n(2nn). For n=0, this says p(0)(z,z)=1, as required by [F9].

1.5F1F8F9given

A sum of an odd number of ±1 increments is odd and cannot be zero. Thus p(2n+1)(z,z)=0 for every n≥0.

2.1F14step 1.4given

Put an:=p(2n)(z,z). By step 1.4 and [F14], πn an→1 as n→∞. Hence there is N≥1 such that for n≥N, an≥12πn.

3.1step 1.5step 2.1given

For each integer m≥1, there are 2m+1 integers in [m2,(m+1)2), and for each one n−1/2≥(m+1)−1. Therefore ∑n=m2(m+1)2−1n−1/2≥2m+1m+1>1. Infinitely many such disjoint blocks show ∑n≥Nn−1/2=∞; [step 2.1] then gives ∑n≥0p(2n)(z,z)=∞. The odd-time terms are zero by step 1.5, so the full Green series ∑n≥0p(n)(z,z) diverges. The initial n=0 term is 1 and is included.

4.1F11F12F13step 3.1given

By [F11], divergence of the Green series implies that this fixed state z is recurrent; by [F12] and [F13], this means exactly Pz(Tz+<∞)=1. Since z was arbitrary, the conclusion holds for every state of Z. This uses only the forward implication of [F11], and no converse to the corollary is asserted.

5.1A1F1F6F8F9F11F12F13step 1.1step 1.3step 1.5step 3.1step 4.1given∎

The state space is fixed as Z, which contains 0 and infinitely many integers, so the empty-space and one-state cases are inapplicable. Every row has two distinct positive transitions, so the walk is neither absorbing nor deterministic. Zero return weights occur at every odd time by step 1.5; the time-zero Green term is 1 but is not a positive-time return [F12, F13]. AC [A1] is used exactly for the canonical path law and the stated finite-dimensional and recurrence suppliers. The enumeration of Z, the finite path count, and the square-block divergence require no choice. The only iff input is [F11]; the proof uses its divergence-to-recurrence direction, so the reverse direction is not part of the claim.

Source notes

Durrett, Probability: Theory and Examples, 5th ed., §5.4, Theorem 5.4.3 and complete proof (printed pp. 288–289/PDF pp. 295–296, official parser lines 19430–19460) gives the general return-series criterion for random walks. The d=1 part of Theorem 5.4.4 (printed p. 289/PDF p. 296, lines 19461–19469) uses odd-time parity and the central-order return probability to conclude recurrence. Its asymptotic is cited there from Theorem 3.1.3; here the published central-binomial asymptotic is used and the divergence is proved by square blocks. Durrett's source proof supports the result but does not replace the local kernel construction, finite-word calculation, or statewise Green-series argument above.

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