Design of quantizers for decentralized estimation systems

By W. Lam, Amy R. Reibman. 1993

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Learning Distributions from their Samples under Communication Constraints (2019, methodology): “There has been some previous work in analyzing Fisher information from a quantized scalar random variable such as [7], [8], [9], [10].”
Minimax Quantization for Distributed Maximum Likelihood Estimation (2006, methodology): “When the parameter is random, Lam and Reibman[2] developed an iterative quantizer that maximizes the Bayesian Fisher Information.”
Universal decentralized estimation in a bandwidth constrained sensor network (2005, background): “In the case where the communication links can only transmit discrete signals, the work of [3], [10], [11], [15] addressed various design and implementation issues using the knowledge of joint distribution of sensor data.”
A Geometric Characterization of Fisher Information from Quantized Samples with Applications to Distributed Statistical Estimation (2018, methodology): “There has been some previous work in analyzing Fisher information from a quantized scalar random variable such as [5],[6],[7],[8].”
Quantization For Distributed Estimation in Large Scale Sensor Networks (2005, methodology): “In the distributed estimation scenario, Lam and Reibman in [4] developed an iterative quantizer to maximize the Bayesian Fisher Information of a random parameter.”