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T. H. Chan

dblp:69/2601 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2003
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Theory of computation · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Theoretical computer science
1 paper
Information theory · 100%

Topics — the 1 heaviest of 1, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Information theory › information measures › information inequalities
linear information inequalities
0.012003
Balanced information inequalities · IEEE Trans. Inf. Theory 2003

Methods — techniques the papers use, named apart from their topics

residual weight decomposition · 0.0
YearPublicationVenuePosition
2003 Balanced information inequalities
abstract
In this correspondence, we are interested in linear information inequalities, both discrete and continuous ones. We show that every discrete information inequality is associated with a "balanced" information inequality and a set of "residual weights." To prove the inequality, it is necessary and sufficient to prove that its "balanced" version is valid and all its residual weights are nonnegative. For a continuous information inequality, we prove that it is valid if and only if its discrete counterpart is balanced and valid.
T. H. Chan
IEEE Trans. Inf. Theory1