Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

R. N. Jackson

dblp:99/10289 · DBLP profile ↗
← Back
1ranked-venue papers
1as first author
0since 2021 · last 1976
—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 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Information theory
estimation theory
0.011976
Optimal linear estimation with uncertain observations (Corresp.) · IEEE Trans. Inf. Theory 1976
Information theory › estimation theory
linear estimation
0.011976
Optimal linear estimation with uncertain observations (Corresp.) · IEEE Trans. Inf. Theory 1976
Information theory › statistical inference › detection and estimation
uncertain observation
0.011976
Optimal linear estimation with uncertain observations (Corresp.) · IEEE Trans. Inf. Theory 1976
Information theory › probability theory
correlated random variable
0.011976
Optimal linear estimation with uncertain observations (Corresp.) · IEEE Trans. Inf. Theory 1976

Methods — techniques the papers use, named apart from their topics

minimum mean-square estimation · 0.0correlation analysis · 0.0
YearPublicationVenuePosition
1976 Optimal linear estimation with uncertain observations (Corresp.)
abstract
Nahi [2] considered the optimal linear estimation with uncertain observations. In his model, the binary random variables characterizing uncertainty of signals in the observation were statistically independent. This correspondence considers the same model but with the binary random variables correlated.
R. N. Jackson, D. N. Prabhakar Murthy
IEEE Trans. Inf. Theory1