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David J. Clements

dblp:61/8096 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Information theory
estimation theory
0.011975
A nonlinear fixed-lag smoother for finite-state Markov processes · IEEE Trans. Inf. Theory 1975
Information theory › estimation theory › state estimation
fixed-lag smoothing
0.011975
A nonlinear fixed-lag smoother for finite-state Markov processes · IEEE Trans. Inf. Theory 1975
Information theory › probability theory › stochastic processes
markov processes
0.011975
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
0.011975
A nonlinear fixed-lag smoother for finite-state Markov processes · IEEE Trans. Inf. Theory 1975
Mathematical optimization
smoothing
0.011975
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
YearPublicationVenuePosition
1975 A nonlinear fixed-lag smoother for finite-state Markov processes
abstract
The 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. Theory1