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One-dimensional simple symmetric walk is recurrent
Statement
Assume AC. Identify with and define, for , For each fixed , use the canonical chain law with initial measure and kernel . This is the simple symmetric nearest-neighbor walk in dimension one (Simple symmetric walk on the integer lattice). With , every state is recurrent:
Facts & Assumptions
Given: AC, the state space with its full power-set sigma-algebra, the one-dimensional simple symmetric walk, and a fixed start .
AC is assumed by the canonical path-law and finite-dimensional-law suppliers used here. (The Axiom of Choice)
In dimension one, the simple symmetric transition row has mass at each of the two distinct neighbors , and zero elsewhere. (Simple symmetric walk on the integer lattice)
For each point , is a probability measure. (A Dirac set function is a probability measure)
A finite nonnegative weighted sum of measures is a measure. (Nonnegative scalar multiples and countable weighted sums of measures are measures)
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)
A function is measurable when the preimage of each measurable target set is measurable in the source space. (A measurable function between measurable spaces)
Under AC, the path space carries the canonical law for a specified probability initial measure and probability kernel. (Canonical Markov chain on path space)
With initial measure , the fixed-start notation is and almost surely. (Initial distribution of a Markov chain)
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)
The matrix entries are and , with . (Transition matrices and n-step probabilities)
is the number of -element subsets of an -element set. (The set of -element subsets and the binomial coefficient )
For a fixed state, recurrence is equivalent to divergence of its return Green series: is recurrent iff . (Equivalent criteria for recurrence and transience)
Recurrence means that the positive-time return probability is one. (Recurrent and transient states)
is the strictly positive return time. (Hitting, return, and visit times)
A set in bijection with is countably infinite and hence at most countable. (Finite, countably infinite, countable, uncountable)
Proof
Define by , , and for . Every integer occurs exactly once, so this is a bijection and is countably infinite, hence at most countable as required by the chain-law and recurrence suppliers.
For each fixed , the two Dirac measures in the displayed definition of are probability measures [F2]; their weighted sum is a measure by [F3], and its total mass is . For each fixed , the map is measurable because every subset of the discrete source is measurable [F5]. Thus [F4] makes a probability kernel. Its singleton entries agree with [F1], so this is exactly the d=1 simple symmetric walk kernel.
Under AC [A1], [F6] supplies the canonical law with initial measure for each fixed ; [F7] names it , and [F8] gives the probabilities of its finite path cylinders. The transition matrix of this chain is the matrix in [F9].
Fix and . A sign word determines the path for . Each such cylinder has probability by [F1], [F7], and [F8]. Distinct sign words give disjoint cylinders, and their endpoint equals exactly when the word has equally many and entries. By [F10] there are such words. Consequently For , this says , as required by [F9].
A sum of an odd number of increments is odd and cannot be zero. Thus for every .
Put . By step 1.4 and [F14], as . Hence there is such that for ,
For each integer , there are integers in , and for each one . Therefore Infinitely many such disjoint blocks show ; [step 2.1] then gives . The odd-time terms are zero by step 1.5, so the full Green series diverges. The initial term is and is included.
By [F11], divergence of the Green series implies that this fixed state is recurrent; by [F12] and [F13], this means exactly . Since was arbitrary, the conclusion holds for every state of . This uses only the forward implication of [F11], and no converse to the corollary is asserted.
The state space is fixed as , which contains 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 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 , 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.
Depends on
- The Axiom of Choice
- The set $[A]^{k}$ of $k$-element subsets and the binomial coefficient $\binom{n}{k} := \lvert [n]^{k}\rvert$
- Finite, countably infinite, countable, uncountable
- Hitting, return, and visit times
- Initial distribution of a Markov chain
- A measurable function between measurable spaces
- Measure kernel and probability kernel
- Recurrent and transient states
- Simple symmetric walk on the integer lattice
- Transition matrices and n-step probabilities
- A Dirac set function is a probability measure
- Nonnegative scalar multiples and countable weighted sums of measures are measures
- Canonical Markov chain on path space
- Finite-dimensional laws of a Markov chain
- Equivalent criteria for recurrence and transience
- The central binomial coefficient is asymptotic to 4^n divided by the square root of pi n
Used by
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Sources
- Durrett, Probability: Theory and Examples, fifth edition (standard reference, not scraped)