Year: 2013
Type: article
Abstract: A protocol for distributed estimation of discrete distributions is proposed. Each agent begins with a single sample from the distribution, and the goal is to learn the empirical distribution of the samples. The protocol is based on a simple message-passing model motivated by communication in social networks. Agents sample a message randomly from their current estimates of the distribution, resulting in a protocol with quantized messages. Using tools from stochastic approximation, the algorithm is shown to converge almost surely. Examples illustrate three regimes with different consensus phenomena. Simulations demonstrate this convergence and give some insight into the effect of network topology.
Authors: Sarwate, Anand D., Javidi, Tara
Institutions: Princeton University, University of California San Diego
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: CIF: Small: Controlling Uncertainty: On the Sequential Refinement of Belief, CPS: Synergy: Collaborative Research: Event-Based Information Acquisition, Learning, and Control in High-Dimensional Cyber-Physical Systems, G2088071282, RI: FWGrid: A Research Infrastructure with Fast Wireless, Wired, Compute, and Data Infrastructure for Next Generation Systems and Applications, CIF: Small: Collaborative Research: Inference by social sampling
OpenAlex ID: W2951451113