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.
Random and systematic Gibbs scan schemes
Definition
Inputs: A common finite-product measurable state space with target probability , an integer , and for each a nonempty block with its supplied proper full conditional kernel for . Invoke Exact Gibbs block update for each block to obtain its exact update kernel . Select random scan, systematic scan, or both; if random scan is selected, also supply fixed weights with .
Admissible conditions: Every acts on this same and is an exact block-update kernel for this same target . The weights are required only for the random-scan branch; systematic scan takes no weights.
Outputs: For each selected mode, its one-update random-scan kernel or full-sweep systematic kernel ; for systematic scan, also the phase-indexed single-update kernels .
Random-scan instructions: At each update, independently draw with probabilities and apply to the current state. The one-update kernel is Uniform random scan is the special case .
Systematic-scan instructions: Starting from the current state, apply , then , through in that fixed order; repeat the same order for each subsequent sweep. For kernels, means chronological composition: apply first and last, as in Composition of probability kernels. Return the full-sweep kernel . Observing after each single update gives the phase-indexed sequence and is generally time-inhomogeneous; observing only at full-sweep boundaries gives a homogeneous chain with kernel . The scan kernels are well-defined by Random and systematic scan kernels are well-defined ↗.
Depends on
Used by
Dependency tree · two levels
10 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
- Art B. Owen, Monte Carlo Theory, Methods and Examples, Chapter 12 (standard reference, not scraped)
- Gareth O. Roberts and Jeffrey S. Rosenthal, General State Space Markov Chains and MCMC Algorithms (standard reference, not scraped)