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.
Finite weighted trellises
Definition
A finite weighted trellis has an integer length . If , it is the empty structure, with no vertex layers, edges, or endpoint weights. If , it consists of finite vertex sets , edge sets for , positive edge weights , and initial and terminal weights and . The sets may be empty. A zero initial or terminal weight disables that endpoint; every edge in is allowed and has positive weight.
A path for is a tuple with , , , and for every . Its score is where the empty product for is . For , the unique path is the empty tuple, with score (the empty product).
A prefix path through layer , for , satisfies the same conditions through , without a terminal-weight condition. For , the empty tuple is the unique prefix path through no layers, with score . A vertex is reachable if some prefix path ends at ; otherwise it is unreachable. There are finitely many paths. If at least one path exists, its maximum score is attained; if none exists, the trellis has no admissible path.
Used by
- Max-product Viterbi decoding on a finite trellis Algorithm
- Viterbi sequence decoding for a finite hidden Markov model Algorithm
- A finite trellis with tied maxima and an unreachable high-score state Example
- The optimal-prefix recurrence for a finite trellis Lemma
- Finite-trellis Viterbi decoding returns a maximum-score path Theorem
Dependency tree · 0 levels
Nothing. This result depends on no other item in the library.
Sources
- Manolis Kellis, 6.096 Algorithms for Computational Biology, Lecture 7: Gene Finding and HMMs, MIT OpenCourseWare (Spring 2005) (standard reference, not scraped)
- Stanford CS262, Lecture 7: Hidden Markov Models (standard reference, not scraped)