How statement and proof provenance work
The first chip identifies the source of the statement or construction; the second identifies the source of its local proof or verification.
- Literature-sourced: the exact statement appears in a cited source; only wording and notation differ.
- AI-adapted: a semantically identical restatement of literature-sourced material, modulo indexing, notation, and boundary cases adopted by the library.
- AI-generated: a genuinely novel statement formulated by AI, with no source for the claim itself.
These labels describe origin, not correctness: citations and verification chips remain separate evidence.
Closed-class stationary laws and initial-law-dependent Cesàro weights
Statement
Let be a finite real column-stochastic matrix, let be its row transition matrix, and let be the closed classes of the positive-transition graph defined in Finite Markov closed-class graph decomposition. Let be the remaining transient states. Each has a unique invariant probability row vector , extended by zero off . Every invariant probability row vector for is exactly
For an initial row probability , set and for . If , then
and if this means . The Cesàro averages converge to . The weights are determined by the initial law; when there is more than one closed class, there is no single stationary vector selected by the transition matrix alone.
Facts & Assumptions
Given: A finite column-stochastic matrix , its equivalent row transition matrix , and an initial row probability vector .
Finite column-stochastic matrices and probability vectors and Transition matrices and n-step probabilities: P is column-stochastic, K is row-stochastic, and row laws evolve by multiplication by K.
Finite Markov closed-class graph decomposition defines the positive-transition graph by when ; its strongly connected components partition the finite state set, and a component is closed exactly when it has no outgoing edge.
Finite-step escape from a transient substochastic block: for some and , and, writing , ; hence .
Transient hitting, mean exit, and residual bounds: and the first-exit probability into a class from a transient state is .
An irreducible finite class has a unique invariant vector: every closed irreducible class has one positive invariant probability, and its real invariant row vectors are exactly scalar multiples of that probability.
Invariant and stationary distribution for a Markov kernel: invariant row probabilities are exactly the probability measures satisfying .
Bolzano-Weierstrass: every bounded real sequence has a convergent subsequence: every bounded real sequence has a convergent subsequence.
Proof
Given: P, K, the class partition, and initial law u as in the statement.
Proof technique: use finite-step escape for transient mass, classwise uniqueness for invariant laws, and compactness of the finite probability simplex for Cesàro limits.
The finite graph's strongly connected components form a partition, and its component graph has no directed cycle: a cycle would make its components mutually reachable and hence one component. Following outgoing edges from any component must therefore terminate at a sink, since the component graph is finite and acyclic. A sink is exactly a closed component by [F2]. From any state outside the closed classes, follow a component path to a sink. Each component edge is realized by a positive transition, and strong connectivity supplies finite positive paths from the current state to the source of each such transition and from its endpoint to the next transition's source. Concatenating these paths gives a positive-transition path into a closed class. Before its first entry, this path stays in U because a closed class has no outgoing edge. If U is nonempty, [F3] applies to T. Writing k=aL+r as in [F3] gives , so the Neumann series converges and is .
For each closed class , the restriction is a finite irreducible row-stochastic matrix. By [F5] it has a unique invariant probability . Extending by zero outside preserves invariance because no transition leaves the class. Thus every convex combination in the statement is invariant.
Let be any invariant probability row vector and write its coordinates as . Since closed classes have no transitions to U, the stationary equation on the transient coordinates is . If U is nonempty, [F3] gives for some , and therefore . Hence . Since no closed class has transitions to another closed class, the stationary equation now restricts separately to each block . Thus each is invariant on its class, so [F5] makes it with ; their masses sum to one. This proves the asserted full set of invariant probabilities.
For each transient state i, the probability weight of first entering class after k transient transitions is . These disjoint first-entry weights sum, by [F4], to . Initial mass already in remains there, so the total class weight from u is . Moreover , since every transition from a transient state either remains in U or enters exactly one of the closed classes; thus . If U is empty, the same formula reduces directly to .
Define . Every coordinate lies in [0,1]. From any subsequence, apply [F7] to its first coordinate, restrict to that subsequence, and repeat for the finitely many remaining coordinates; the final subsequence converges coordinatewise. Also , whose ℓ1 norm is at most . Hence every subsequential limit is an invariant probability.
The mass is nondecreasing because is closed; its increment at each step is the first-entry weight from step 1.4. The partial sums of these weights tend to by [F3]–[F4], and the unabsorbed transient mass tends to zero, so . For any η>0, choose N₀ so |uKⁿ(Cⱼ)−αⱼ|<η for n≥N₀; the average error is at most the finite sum of the first N₀ errors divided by N, plus η, and hence tends to at most η. Since η is arbitrary, the Cesàro class mass tends to αⱼ. Thus every subsequential limit has for each j. By step 1.3 this invariant probability must be .
Every convergent subsequence of has the same limit from step 2.1. If the full sequence did not converge to that limit, there would be ε>0 and a subsequence staying at least ε away. Its finitely many bounded coordinates admit a convergent further subsequence by repeated finite applications of [F7]; step 2.1 forces that further limit to be the stated mixture, a contradiction. Thus the full Cesàro sequence converges to the initial-law-dependent mixture stated.
Depends on
- Finite column-stochastic matrices and probability vectors
- Transition matrices and n-step probabilities
- Invariant and stationary distribution for a Markov kernel
- Finite Markov closed-class graph decomposition
- Finite-step escape from a transient substochastic block
- Transient hitting, mean exit, and residual bounds
- An irreducible finite class has a unique invariant vector
- Bolzano-Weierstrass: every bounded real sequence has a convergent subsequence
Used by
Dependency tree · two levels
19 results within two dependency steps of this one, each drawn at its shortest distance from it. An arrow runs from a result to what uses it, so the chart reads left to right and ends at this result, which carries a heavier outline. Every node is a link to that result. Click elsewhere on the chart to enlarge it.
Sources
- Charles M. Grinstead and J. Laurie Snell, Introduction to Probability, Chapter 11 (standard reference, not scraped)