Skip to main content
We gratefully acknowledge support from the Simons Foundation, member institutions, and all contributors. Donate
arXiv logo
Cornell University Logo

Computer Science > Information Theory

arXiv:1504.06028 (cs)
[Submitted on 23 Apr 2015]

Title:Converses for distributed estimation via strong data processing inequalities

Authors:Aolin Xu, Maxim Raginsky
View PDF
Abstract:We consider the problem of distributed estimation, where local processors observe independent samples conditioned on a common random parameter of interest, map the observations to a finite number of bits, and send these bits to a remote estimator over independent noisy channels. We derive converse results for this problem, such as lower bounds on Bayes risk. The main technical tools include a lower bound on the Bayes risk via mutual information and small ball probability, as well as strong data processing inequalities for the relative entropy. Our results can recover and improve some existing results on distributed estimation with noiseless channels, and also capture the effect of noisy channels on the estimation performance.
Comments: 6 pages; to be presented at ISIT 2015
Subjects: Information Theory (cs.IT)
Cite as: arXiv:1504.06028 [cs.IT]
  (or arXiv:1504.06028v1 [cs.IT] for this version)
  https://doi.org/10.48550/arXiv.1504.06028

Submission history

From: Maxim Raginsky [view email]
[v1] Thu, 23 Apr 2015 03:01:30 UTC (62 KB)
Full-text links:

Access Paper:

  • View PDF
  • TeX Source
  • Other Formats
view license
Current browse context:
cs.IT
< prev   |   next >
new | recent | 2015-04
Change to browse by:
cs
math
math.IT

References & Citations

DBLP - CS Bibliography

listing | bibtex
Aolin Xu
Maxim Raginsky
export BibTeX citation

Bookmark

BibSonomy logo Reddit logo

Bibliographic and Citation Tools

Bibliographic Explorer (What is the Explorer?)
Connected Papers (What is Connected Papers?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)

Code, Data and Media Associated with this Article

alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
Papers with Code (What is Papers with Code?)
ScienceCast (What is ScienceCast?)

Demos

Replicate (What is Replicate?)
Hugging Face Spaces (What is Spaces?)
TXYZ.AI (What is TXYZ.AI?)

Recommenders and Search Tools

Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)