Demo Data from EDBT 2026 / reviewers in the wild / expert
David J. Clements
dblp:61/8096
· 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 · 80% Mathematical optimization · 20% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Information theory
estimation theory |
0.0 | 1 | 1975 | A nonlinear fixed-lag smoother for finite-state Markov processes · IEEE Trans. Inf. Theory 1975 |
Information theory › estimation theory › state estimation
fixed-lag smoothing |
0.0 | 1 | 1975 | A nonlinear fixed-lag smoother for finite-state Markov processes · IEEE Trans. Inf. Theory 1975 |
Information theory › probability theory › stochastic processes
markov processes |
0.0 | 1 | 1975 | A nonlinear fixed-lag smoother for finite-state Markov processes · IEEE Trans. Inf. Theory 1975 |
Information theory › probability theory › stochastic processes › markov processes
random telegraph signal |
0.0 | 1 | 1975 | A nonlinear fixed-lag smoother for finite-state Markov processes · IEEE Trans. Inf. Theory 1975 |
Mathematical optimization
smoothing |
0.0 | 1 | 1975 | A nonlinear fixed-lag smoother for finite-state Markov processes · IEEE Trans. Inf. Theory 1975 |
Methods — techniques the papers use, named apart from their topics
time-discretization · 0.0fixed-point smoothing · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1975 | A nonlinear fixed-lag smoother for finite-state Markov processesabstractThe fixed-lag smoothing of random telegraph type signals is studied. The smoothers are derived by first obtaining fixed-point smoothing equations and then using a time discretization. Simulation results are described that verify the qualitative carry-over of known results for the linear-Gaussian problem: the greater the lag, the greater the improvement; beyond a certain lag, no further improvement is obtained by the increase of lag; and the higher the signal-to-noise ratio (SNR), the greater is the improvement over filtering obtained through the use of smoothing. Smoothing errors of one-half the corresponding filtering error are demonstrated. David J. Clements, Brian D. O. Anderson |
IEEE Trans. Inf. Theory | 1 |