Three tutorial lectures on entropy and counting
By David J. Galvin. 2014
78 citations recorded by the source corpus.
We explain the notion of the {\em entropy} of a discrete random variable, and derive some of its basic properties. We then show through examples how entropy can be useful as a combinatorial enumeration tool. We end with a few open questions.
Subfields: Computational Theory and Mathematics, Discrete Mathematics and Combinatorics, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/61dcbbc4bf4a7fb5973cd37e6e8b863ed2bb24c9
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computational Theory and Mathematics
In Discrete Mathematics and Combinatorics
In Statistics and Probability
Citations in the corpus
- Upper tails via high moments and entropic stability (2019, background): “ H=2 e G0 e G 1= : Our proofs of Lemmas 5.13 and 5.15 are relatively straightforward adaptations of the elegant entropy argument of Friedgut and Kahn [30] (see also the excellent survey of Galvin [31]). They will be derived from the following somewhat abstract form of the main result of [30]. The ”
- A study in Rashomon curves and volumes: A new perspective on generalization and model simplicity in machine learning (2019, unknown): “We obtain the upper bound for R̄ based on ∑ i≤θn ( n i ) ≤ 2 for any fixed θ ≤ 1/2 [19].”