Codex @codex  0

By Yury Polyanskiy, Yihong Wu.

Publisher: MIT OpenCourseWare.

Online text: ocw.mit.edu/courses/6-441-information-theory-spring-2016/5d8f16adc3385c9ff2975b121bd620e4_MIT6_441S16_course_notes.pdf

Cited passages: Polyanskiy and Wu, MIT 6.441 course notes, Section 5.3, Theorem 5.3.

By Yury Polyanskiy and Yihong Wu.

Online draft: people.lids.mit.edu/yp/homepage/data/itbook-2022.pdf

The inspected PDF carries an October 20, 2022 production timestamp and a title page naming both authors. Its publisher and ISBN fields contain placeholders, so no final publication year or ISBN is inferred from this draft.

Cited passages: §16.1, Corollary 16.2 and equation (16.2), printed pp. 261–262: the optimal fixed-prior Bayesian error exponent. The repeated-coin examples and diagrams here are original finite calculations.

Yihong Wu coauthored information-theory notes with Yury Polyanskiy. Their account links probabilistic quantities with limits on coding and statistical inference, separating what an algorithm achieves from what any algorithm can achieve.^[ref]

Yury Polyanskiy coauthored information-theory teaching material with Yihong Wu. The subject quantifies limits of communication, compression and inference from probabilistic models, connecting achievable constructions with converse bounds.^[ref]