Multiple Objects: Error Exponents in Hypotheses Testing and Identification
Evgueni Haroutunian, Parandzem Hakobyan
TL;DR
This paper reviews research on optimal hypothesis testing for multiple objects, applying Shannon information theory techniques to solve complex statistical problems involving multiple hypotheses.
Contribution
It provides a comprehensive survey of methods and results in multiple-object hypothesis testing, highlighting the application of information theory to statistical decision problems.
Findings
Optimal error exponents for multiple hypotheses
Application of Shannon information theory techniques
Insights into multi-object model testing
Abstract
We servey a series of investigations of optimal testing of multiple hypotheses conserning various multiobject models. These studies are a bright instance of application of methods and technics developed in Shannon information theory to solution of typical statistical problems.
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Taxonomy
TopicsDistributed Sensor Networks and Detection Algorithms · Neural Networks and Applications · Blind Source Separation Techniques