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.
McMillan inequality for finite binary uniquely decodable codes
Statement
Let be a finite binary uniquely decodable code with lengths . Then
Facts & Assumptions
Given: A finite source alphabet , a binary code , and injectivity of its concatenation extension .
Finite binary source codes, extension, and expected length: Unique decodability means that the concatenation extension is injective on all finite source words, and every codeword is nonempty.
Integer part: for every real there is exactly one integer with : For every real , the integer part satisfies .
Proof
Put and . For each integer , expand . For each total length , let count the tuples with . Their concatenated codewords are distinct by [F1] and each is a binary word of length , of which there are exactly because each of the positions has two choices; hence .
Since every , the only possible totals satisfy , so there are possible integer values of . Grouping step 1.1 by gives . Therefore for every .
Suppose , set , and put . By [F2], the integer satisfies and , so . The binomial theorem gives , contradicting step 2.1, which gives . Hence .
Depends on
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
- Stanford EE376A, Information Theory Course Notes, Chapter 4: Lossless Compression (standard reference, not scraped)
- MIT OpenCourseWare 6.450, Memory-less sources, prefix free codes, and entropy (standard reference, not scraped)