Abstract
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.
Original language | English |
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Pages (from-to) | 623-644 |
Number of pages | 22 |
Journal | IEEE journal on selected areas in information theory |
Volume | 5 |
DOIs | |
State | Published - 2024 |
Keywords
- Memory complexity
- communication complexity
- entropy estimation
- finite memory algorithms
- inference under memory constraints
- parameter estimation
- sample complexity
- simple hypothesis testing
- uniformity testing
All Science Journal Classification (ASJC) codes
- Artificial Intelligence
- Applied Mathematics
- Computer Networks and Communications
- Media Technology