Demo Data from SIGIR 2026 / reviewers in the wild / expert
J. A. Edward
dblp:134/8233
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 1973
—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 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Information theory › signal processing › spectral estimation
maximum entropy spectrum |
0.0 | 1 | 1973 | Notes on maximum-entropy processing (Corresp.) · IEEE Trans. Inf. Theory 1973 |
Information theory › signal processing
spectral estimation |
0.0 | 1 | 1973 | Notes on maximum-entropy processing (Corresp.) · IEEE Trans. Inf. Theory 1973 |
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1973 | Notes on maximum-entropy processing (Corresp.)abstractMaximum-entropy processing is a method for computing the power density spectrum from the firstNlags of the autocorrelation function. Unlike the discrete Fourier transform, maximum-entropy processing does not assume that the other lag values are zero. Instead, one mathematically ensures that the fewest possible assumptions about unmeasured data are made by choosing the spectrum that maximizes the entropy for the process. The use of the maximum entropy approach to spectral analysis was introduced by Burg [1]. In this correspondence, the authors derive the maximum-entropy spectrum by obtaining a spectrum that is forced to maximize the entropy of a stationary random process. J. A. Edward, Michael M. Fitelson |
IEEE Trans. Inf. Theory | 1 |