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Antonín Otáhal

dblp:23/2508 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 1996
—ORCID · none

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

Databases, data management, data science and information retrieval · 1Theory 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 · 75% Mathematical optimization · 25%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Mathematical optimization › numerical analysis
error estimation		 0.011996
Minimum entropy of error estimation for discrete random variables · IEEE Trans. Inf. Theory 1996
Information theory
hypothesis testing		 0.011996
Minimum entropy of error estimation for discrete random variables · IEEE Trans. Inf. Theory 1996
Information theory › information measures › entropy › generalized entropy
minimum entropy		 0.011996
Minimum entropy of error estimation for discrete random variables · IEEE Trans. Inf. Theory 1996
Information theory › hypothesis testing › binary hypothesis testing
neyman-pearson detection		 0.011996
Minimum entropy of error estimation for discrete random variables · IEEE Trans. Inf. Theory 1996
YearPublicationVenuePosition
1996 Minimum entropy of error estimation for discrete random variables
abstract
The principle of minimum entropy of error estimation (MEEE) is formulated for discrete random variables. In the case when the random variable to be estimated is binary, we show that the MEEE is given by a Neyman-Pearson-type strictly monotonous test. In addition, the asymptotic behavior of the error probabilities is proved to be equivalent to that of the Bayesian test.
Martin Janzura, Timo Koski, Antonín Otáhal
 IEEE Trans. Inf. Theory3
1994 Minimum Entropy of Error Principle in Estimation
Martin Janzura, Timo Koski, Antonín Otáhal
 Inf. Sci.3