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.
A finite Metropolis chain can equilibrate within a well and remain far from equilibrium
Example
Assume AC. Fix in reciprocal-energy units and a finite barrier height in the corresponding energy unit, so is dimensionless; put , and let the five states be . Equip with the discrete metric , whose Borel sigma-algebra is ; the target and proposal are defined on the measurable carrier . The energies are and , so the Gibbs law is . Let be the symmetric proposal matrix specified by and for , with all other entries zero. Each row sums to one. Apply the Metropolis–Hastings procedure with target and proposal , and denote its transition matrix by . Write for the left well and, for each , define The Gibbs law is invariant, but the chain started with law has probability of making no exit from during its first steps. Conditional on no exit, its two-state within-well transition flips between and with probability ; the conditional stationary law within the well is uniform, and from either state its within-well total-variation error after such updates is at most . Nevertheless, Thus the conventional worst-start mixing time exceeds , even though the chain can equilibrate quickly within one well. This is an exact finite mathematical example; it reports no simulated or measured data.
Example references
Facts & Assumptions
Given: in reciprocal-energy units, in the corresponding energy unit, the finite discrete state space , the target , and proposal matrix from the Example.
The Axiom of Choice: AC is assumed, as required by the imported general-state Metropolis–Hastings suppliers.
Standard Borel spaces: a measurable space is standard Borel when it is measurably bijective to a Polish space's Borel measurable space.
Polish spaces are separable completely metrizable spaces: a topological space is Polish when it is separable and its topology is induced by a complete metric.
Probability measures and probability spaces: a probability measure has total mass one.
Measure kernel and probability kernel: a probability kernel is a measurable family of probability measures, with mass one at every source state.
Metropolis–Hastings kernel: on a standard Borel space with target probability and proposal probability kernel , the procedure accepts the common part of the forward and reversed proposal flows and retains rejected mass as a self-transition.
Metropolis–Hastings flow is reversible and invariant: under the algorithm's hypotheses, its output is a probability kernel and is invariant; the supplier asserts no irreducibility, uniqueness, convergence-rate, or mixing-time conclusion.
Invariant and stationary distribution for a Markov kernel: invariance means , or on this finite space for every .
Verification
Given: AC, the finite target and symmetric proposal, and the imported Metropolis–Hastings algorithm and invariance result.
The finite discrete metric on is complete and separable, so [F2] makes it Polish; the identity map then verifies [F1]. The weights sum to , so is a probability measure by [F3]. Each row of is nonnegative and sums to one, and on a finite discrete space every function is measurable; thus [F4] makes a probability kernel. Consequently the inputs satisfy [F5].
For states with , the forward proposal-flow mass is and the reversed mass is . On a finite set the greatest measure dominated by both has point mass , so [F5] accepts with probability . Symmetry of reduces this to . Hence each and jumps to its partner with probability , jumps to with probability , and stays put with probability ; from it jumps to each of the four well states with probability .
By [F6] and [F7], is invariant for . Also for all : every state reaches in at most two positive-probability transitions, reaches every state in at most two, and any unused steps can be filled by the positive self-transition at . Since is finite, let , , and . If , each row of is times the uniform law plus times a probability row. For with total mass zero, its positive and negative parts each have mass ; since every residual row assigns a number in to an event, integration against is bounded in absolute value by . Thus for every two laws , . If , all rows are uniform. Therefore is the unique invariant law and the chain converges to it.
The target assigns equal mass to , so is uniform and . From either state in , the exit probability to is , while the transition to the other state of has probability . Conditioning a step on no exit gives flip probability and stay probability . This symmetric two-state chain preserves the uniform law and has nonconstant eigenvalue , giving the stated within-well total-variation bound. The exit probability is constant across both states, so survival for steps has probability .
Survival implies , so for the initial law , . On a finite space, if has total mass zero then , so the event gives For any mixture of starting states, the half- triangle inequality bounds its distance from by the largest component distance. Since is the mixture of and with weights , it follows that . If , Bernoulli's inequality gives , while ; hence . The definition of therefore gives the claimed lower bound.
Depends on
The Axiom of Choice
Standard Borel spaces
Polish spaces are separable completely metrizable spaces
Probability measures and probability spaces
Measure kernel and probability kernel
Invariant and stationary distribution for a Markov kernel
Metropolis–Hastings kernel
Metropolis–Hastings flow is reversible and invariant