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14 - Distributed Statistical Inference with Compressed Data

Published online by Cambridge University Press:  22 March 2021

By
Wenwen Zhao  and
Lifeng Lai
Edited by
Miguel R. D. Rodrigues  and
Yonina C. Eldar
Miguel R. D. Rodrigues
Affiliation:
University College London
Yonina C. Eldar
Affiliation:
Weizmann Institute of Science, Israel
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Summary

This chapter introduces basic ideas of information-theoretic models for distributed statistical inference problems with compressed data, and discusses current and future research directions and challenges in applying these models to various statistical learning problems. In these applications, data are distributed in multiple terminals, which can communicate with each other via limited-capacity channels. Instead of recovering data at a centralized location first and then performing inference, this chapter describes schemes that can perform statistical inference without recovering the underlying data. Information-theoretic tools are borrowed to characterize the fundamental limits of the classical statistical inference problems using compressed data directly. In this chapter, distributed statistical learning problems are first introduced. Then, models and results of distributed inference are discussed. Finally, new directions that generalize and improve the basic scenarios are described.

Keywords

distributed statistical inferencedistributed detectiondistributed estimationdistributed hypothesis testingdistributed source coding
Type
Chapter
Information
Information-Theoretic Methods in Data Science , pp. 425 - 454
Publisher: Cambridge University Press
Print publication year: 2021

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