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Bruno O. Shubert

dblp:134/4389 · DBLP profile ↗
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4ranked-venue papers
4as first author
0since 2021 · last 1975
—ORCID · none

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

Theory of computation · 3 · 3 first-authorHuman-computer interaction and ubiquitous computing · 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
2 papers
Information theory · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Information theory
hypothesis testing		 0.021974
Finite-memory classification of Bernoulli sequences using reference samples (Corresp.) · IEEE Trans. Inf. Theory 1974
Testing a simple symmetric hypothesis by a finite-memory deterministic algorithm · IEEE Trans. Inf. Theory 1973
Information theory › pattern recognition
finite-memory classification		 0.011974
Finite-memory classification of Bernoulli sequences using reference samples (Corresp.) · IEEE Trans. Inf. Theory 1974
Information theory › hypothesis testing › sequential hypothesis testing
finite-memory hypothesis testing		 0.011973
Testing a simple symmetric hypothesis by a finite-memory deterministic algorithm · IEEE Trans. Inf. Theory 1973

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

stopping rules · 0.0randomized algorithm · 0.0finite-memory deterministic algorithms · 0.0deterministic finite-memory algorithms · 0.0
YearPublicationVenuePosition
1975 A Flow-Graph Formula for the Stationary Distribution of a Markov Chain
abstract
It is shown that a stationary distribution of a regular Markov chain can be obtained directly from its transition graph. The technique is similar to signal flow-graph methods, however, it uses trees of the graph rather than loops. The proof is direct and simple.
Bruno O. Shubert
 IEEE Trans. Syst. Man Cybern.1
1974 Finite-memory classification of Bernoulli sequences using reference samples (Corresp.)
abstract
It is shown that in two-way Bernoulli classification problems deterministic machines can perform as well as optimal randomized machines if their memory is increased by less than one bit. This is accomplished by allowing the algorithm to observe samples from both classes, thus in effect using the data source itself to provide the necessary randomization. An application to a simple communication problem is indicated.
Bruno O. Shubert
 IEEE Trans. Inf. Theory1
1973 Testing a simple symmetric hypothesis by a finite-memory deterministic algorithm
abstract
A class of finite-memory deterministic algorithms is introduced and investigated. Optimum algorithms are found for small numbers (up to 21) of states. The algorithms provide their own stopping rules.
Bruno O. Shubert, Calvin Marion Anderson
 IEEE Trans. Inf. Theory1
1970 Review of 'Knowing and Guessing, A Quantitative Study of Inference and Information' (Watanabe, S.; 1969)
Bruno O. Shubert
 IEEE Trans. Inf. Theory1