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TheoremStatement: Literature-sourcedProof: AI-adaptedPipeline-generatedprecheck passaudited 2026-10-02
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Time reversal of a stationary Markov chain

Statement

Assume AC (The Axiom of Choice). Let p be a countable transition matrix (Transition matrices and n-step probabilities) with invariant probability π (Invariant and stationary distribution for a Markov kernel), let X be a p-chain started in π and let E+:={x∈E:π(x)>0}. Define the reverse kernel on E+ by

p∗(x,y):=π(y) p(y,x)π(x),x∈E+, y∈E.

Then:

  1. p∗ extended by p∗(x,y)=0 for y∉E+ is a transition matrix on E+, and π restricted to E+ is invariant for it; no mass ever leaves E+.
  2. Every finite path segment of X read backward is distributed as a p∗-chain started in π: for every r≥1 and 0≤n0<⋯<nr, L(Xnr,…,Xn0)=L(X0∗,Xnr−nr−1∗,…,Xnr−n0∗), where X∗ is a stationary p∗-chain with initial law π.
  3. Detailed balance for π and p (Reversible measure and detailed balance) is equivalent to p∗(x,y)=p(x,y) for all x,y∈E+.
  4. Rows at states outside E+ carry no stationary mass and may be chosen arbitrarily (for instance all equal to δy0 for a fixed y0∈E+) if a kernel on all of E is desired.

Facts & Assumptions

Given: AC, a countable E, a transition matrix p with invariant probability π, a p-chain X started in π, and E+={x:π(x)>0}.

[A1]

Every family of nonempty sets has a choice function; AC is assumed and is used through the stationary-chain, finite-dimensional-law and chain-construction suppliers [F2], [F3] and [F5]. (The Axiom of Choice)

[F1]

On a countable state space π is invariant exactly when π(y)=∑x∈Eπ(x)p(x,y) for every y∈E, and rows of p sum to one with p(x,y)≥0. (Invariant and stationary distribution for a Markov kernel, Transition matrices and n-step probabilities)

[F2]

If a chain has invariant initial law π, then every finite-dimensional law is shift-invariant; in particular Xn has law π for every n. (Invariant initial law makes a Markov chain stationary)

[F3]

Assume Choice. For a K-chain with initial law μ, times 0≤n0<⋯<nr and bounded measurable fj, E∏j=0rfj(Xnj)=∫Eμ(dx)∫EKn0(x,dx0)f0(x0)∏j=1r∫EKnj−nj−1(xj−1,dxj)fj(xj). (Finite-dimensional laws of a Markov chain)

[F4]

A state measure μ satisfies detailed balance for p when μ(x)p(x,y)=μ(y)p(y,x) for all x,y. (Reversible measure and detailed balance)

[F5]

Assume Choice. For an initial probability and a probability kernel, a canonical chain exists on the product path space with that initial law and kernel. (Canonical Markov chain on path space)

Proof

Given: AC, a countable E, a transition matrix p with invariant probability π, and a stationary p-chain X started in π.

Proof technique: check that the normalized backward transition ratios form a stochastic matrix preserving π, then verify the reversal by telescoping products of transition probabilities, and read off the detailed-balance equivalence.

1.1F1given

If x∈E+ and y∉E+ then p(x,y)=0: otherwise π(y)=∑zπ(z)p(z,y)≥π(x)p(x,y)>0 by [F1], contradicting π(y)=0. Hence ∑y∈E+p(x,y)=1 for every x∈E+.

1.2F1given

The formula p∗(x,y)=π(y)p(y,x)/π(x) is well defined for x∈E+ because π(x)>0, and nonnegative; for x∈E+ its row sum is ∑y∈Ep∗(x,y)=1π(x)∑yπ(y)p(y,x)=(πp)(x)π(x)=π(x)π(x)=1, using [F1]; since π(y)=0 for y∉E+, the extended entries p∗(x,y) vanish off E+, so p∗ is a transition matrix on E+ and no mass leaves E+.

2.1F1step 1.1given

The probability π restricted to E+ is invariant for p∗: for y∈E+, ∑x∈E+π(x)p∗(x,y)=∑x∈E+π(y)p(y,x)=π(y)∑x∈E+p(y,x)=π(y) by step 1.1, while for y∉E+ both sides vanish; this is precisely invariance in the countable form [F1].

2.2F4step 1.1given

Detailed balance equivalence: for x,y∈E+ the identity π(x)p(x,y)=π(y)p(y,x) is equivalent, after dividing by the positive number π(x), to p(x,y)=π(y)p(y,x)/π(x)=p∗(x,y); if x∈E+ and y∉E+, then π(x)p(x,y)=0 by step 1.1 and π(y)p(y,x)=0 because π(y)=0. The case x∉E+, y∈E+ follows by exchanging x and y; if both states are outside E+, both weights vanish. Transitions from outside E+ into E+ need not vanish. Hence detailed balance for π and p holds for all pairs exactly when p∗=p on E+×E+.

3.1F1F2F3F5step 1.1step 2.1algebra

By [F5] construct a p∗-chain X∗ on E+ with initial law π∣E+; it is stationary by [F2] and step 2.1. Consecutive-time reversal: for n≥0 and states x0,…,xn∈E+, [F3] with μ=π (and indicators) gives Pπ(X0=x0,…,Xn=xn)=π(x0)∏i=0n−1p(xi,xi+1); reading the same word backward and using the definition of p∗, π(xn)∏i=0n−1p∗(xi+1,xi)=π(xn)∏i=0n−1π(xi)p(xi,xi+1)π(xi+1)=π(x0)∏i=0n−1p(xi,xi+1) after telescoping cancellation of the π(xi), i=1,…,n−1; paths visiting E∖E+ have probability zero by step 1.1, so L(Xn,…,X0)=L(X0∗,…,Xn∗) for a stationary p∗-chain X∗ with initial law π, by [F3] and step 2.1.

4.1step 3.1given

For every r≥1 and 0≤n0<⋯<nr, step 3.1 at n=nr gives L(Xnr,…,X0)=L(X0∗,…,Xnr∗). Taking the coordinates indexed by 0,nr−nr−1,…,nr−n0 on both sides yields the asserted law of (Xnr,…,Xn0).

5.1step 1.2step 4.1given

Null rows: since π(x)=0 for x∉E+, such a state carries no stationary mass and does not appear in the reversal statements of items 1–3, which only involve paths with positive probability; if a kernel on all of E is wanted, fix y0∈E+ (the set E+ is nonempty because π is a probability) and set p∗(x,⋅):=δy0 for x∉E+, which is a probability row and leaves every assertion about E+ unchanged.

6.1A1F2F3F5step 3.1step 2.2given∎

Boundary and axiom cases: if E+=E (the chain is irreducible and positive recurrent, or more generally π has full support) then no null rows arise and clause 3 compares the two kernels on all of E×E; if E is a singleton, p∗=p=1 and both reversal and detailed balance are trivial; the reversal identity of step 3.1 is symmetric in the two directions and does not presuppose p∗=p, so the statement covers nonreversible stationary chains; AC [A1] is used in constructing X∗ through [F5], proving its stationarity through [F2], and computing finite-dimensional laws through [F3]; and all products and telescoping cancellations are finite, no infinite sum being rearranged.

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