Demo Data from EDBT 2026 / reviewers in the wild / expert
Gerald J. Bierman
dblp:134/5766
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 1975
—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 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Information theory › statistical inference › detection and estimation
estimation and filtering |
0.0 | 1 | 1975 | Fixed memory least squares filtering (Corresp.) · IEEE Trans. Inf. Theory 1975 |
Information theory › signal processing › filtering
least squares filtering |
0.0 | 1 | 1975 | Fixed memory least squares filtering (Corresp.) · IEEE Trans. Inf. Theory 1975 |
Information theory › estimation theory › bayesian estimation
kalman filtering |
0.0 | 1 | 1975 | Fixed memory least squares filtering (Corresp.) · IEEE Trans. Inf. Theory 1975 |
Methods — techniques the papers use, named apart from their topics
square root filtering · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1975 | Fixed memory least squares filtering (Corresp.)abstractBuxbaum has reported on three algorithms for computing least squares estimates that are based on fixed amounts of data. In this correspondence, the filter is arranged as a point-deleting Kalman filter concatenated with the standard point-inclusion Kalman filter. The resulting algorithm is couched in a square root framework for greater numerical stability, and special attention is given to computer implementation. Gerald J. Bierman |
IEEE Trans. Inf. Theory | 1 |