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.
Proper full conditional kernel
Definition
Let be a finite nonempty index set, let be measurable spaces, set with its finite product sigma-algebra, and let be a probability measure on . For a nonempty block , write , , let be the coordinate projection, and define for measurable .
Products and product sigma-algebras use the empty-product convention in The product sigma-algebra and its finite iterates: if , then is the one-point space with its trivial sigma-algebra.
A proper full conditional kernel for block is a supplied probability kernel such that for every product-measurable and every measurable , Here is the unique state whose coordinates on are and whose coordinates on are . This is the event-identity definition of a regular conditional distribution of the block given the retained coordinates, with every kernel section a probability measure at every . It makes no existence claim; the A-page density corollary gives one sufficient case.
Depends on
Used by
- Exact Gibbs block update Algorithm
- A joint density supplies a proper full conditional kernel Corollary
- Proper full conditionals of a nondegenerate bivariate normal law Corollary
- A constrained support can make single-site Gibbs reducible Example
- An invariant Gibbs target can still give reducible dynamics Example
- Full conditionals for a bivariate normal target Example
- A Gibbs refresh is conditionally independent of the old block Lemma
- Each exact Gibbs block update is reversible and invariant Lemma
- The Gibbs block update is a probability kernel Lemma
- A systematic Gibbs sweep preserves the target Theorem
Dependency tree · two levels
17 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)