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R. M. Driver

dblp:134/4391 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 1970
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Theory of computation · 1

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

TopicWeightPapersLastEvidence papers
Information theory › probability theory › stochastic processes › stochastic process representation
karhunen-loève expansion
0.011970
Further decomposition of the Karhunen-Loève series representation of a stationary random process · IEEE Trans. Inf. Theory 1970
Information theory › probability theory › stochastic processes
stochastic process representation
0.011970
Further decomposition of the Karhunen-Loève series representation of a stationary random process · IEEE Trans. Inf. Theory 1970
Information theory
covariance factorization
0.011970
Further decomposition of the Karhunen-Loève series representation of a stationary random process · IEEE Trans. Inf. Theory 1970

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

symmetry properties of covariance matrices · 0.0markov process theory · 0.0
YearPublicationVenuePosition
1970 Further decomposition of the Karhunen-Loève series representation of a stationary random process
abstract
It is shown how the Karhunen-Loève(K-L)series representation for a finite sample of a discrete random sequence, stationary to the second order, may be further decomposed into a pair of series by utilizing certain symmetry properties of the covariance matrix of the sequence. The theory is applied to the particular example of a first-order Markov sequence, the series representation of which has not so far been reported in the literature. The generalization to the case of continuous random functions on a finite interval is similar and is therefore only briefly described.
W. D. Ray, R. M. Driver
IEEE Trans. Inf. Theory2