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  • 14 - Distributed Statistical Inference with Compressed Data

  • Edited by Miguel R. D. Rodrigues, University College London, Yonina C. Eldar, Weizmann Institute of Science, Israel
  • Book:
    Information-Theoretic Methods in Data Science
    Published online:
    22 March 2021
    Print publication:
    08 April 2021, pp 425-454

  • 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.