Statistical Inference With Limited Memory: A Survey

The problem of statistical inference in its various forms has been the subject of decades-long extensive research. Most of the effort has been focused on characterizing the behavior as a function of the number of available samples, with far less attention given to the effect of memory limitations on...

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Bibliographic Details
Published inIEEE journal on selected areas in information theory Vol. 5; pp. 623 - 644
Main Authors Berg, Tomer, Ordentlich, Or, Shayevitz, Ofer
Format Journal Article
LanguageEnglish
Published IEEE 2024
Subjects
communication complexity
entropy estimation
Error probability
Estimation
finite memory algorithms
Inference algorithms
inference under memory constraints
Information theory
Memory complexity
Memory management
Parameter estimation
sample complexity
simple hypothesis testing
Surveys
Testing
uniformity testing
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Summary:The problem of statistical inference in its various forms has been the subject of decades-long extensive research. Most of the effort has been focused on characterizing the behavior as a function of the number of available samples, with far less attention given to the effect of memory limitations on performance. Recently, this latter topic has drawn much interest in the engineering and computer science literature. In this survey paper, we attempt to review the state-of-the-art of statistical inference under memory constraints in several canonical problems, including hypothesis testing, parameter estimation, and distribution property testing/estimation. We discuss the main results in this developing field, and by identifying recurrent themes, we extract some fundamental building blocks for algorithmic construction, as well as useful techniques for lower bound derivations.
ISSN:2641-8770
2641-8770
DOI:10.1109/JSAIT.2024.3481296