Proceedings Article10.1109/CDC.1987.272937
Distributed Bayesian parameter estimation
I. Y. Hoballah,Pramod K. Varshney +1 more
- 01 Dec 1987
- Vol. 26, Iss: 26, pp 2223-2228
4
TL;DR: In this article, the problem of distributed Bayesian parameter estimation is considered and three commonly used cost criteria namely mean square error, absolute error and uniform cost are employed, and optimum estimation rules at the individual sensors and optimum combining rule are obtained.
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Abstract: This paper considers the problem of distributed Bayesian parameter estimation. Three commonly used cost criteria namely mean square error, absolute error and uniform cost are employed. Optimum estimation rules at the individual sensors and optimum combining rule are obtained. Suboptimum solutions when the combining rule is restricted to be a linear one are also derived. A simple example is presented for illustration.
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Citations
Design of quantizers for decentralized estimation systems
Wai-Man Lam,Amy R. Reibman +1 more
TL;DR: The authors restrict the local processors to be quantizers and consider the optimal design of the systems to minimize the estimation error based on the Bayes distortion functions and Fisher's information.
189
Distributed estimation over binary symmetric channels in wireless sensor networks
Guiyun Liu,Bugong Xu,Ming Zeng,Hongbin Chen +3 more
- 01 Jun 2011
TL;DR: The results reveal that the capacity of BSCs greatly influences both the performance of the MLE and the CRLB and both the performances have the symmetric property with respect to the crossover probability of B SCs.
17
Robust estimation of sinusoidal signals with colored noise using decentralized processing
R.L. Kashyap,S.G. Oh,R.N. Madan +2 more
TL;DR: A new technique for the estimation of the number of signals and their central frequencies by using the decentralized processing, when it is known a priori that the observations consist of a finite number of source signals corrupted by additive colored random noise process.
15
Robust estimation of sinusoidal signal with colored noise using decentralized processing
Rangasami L. Kashyap,Sang Oh,Rabinder N. Madan +2 more
- 01 Dec 1987
TL;DR: A new technique for the estimation of the number of signals and their central frequencies by using the decentralized processing, when it is known a priori that the observations consist of a finite number of source signals corrupted by additive colored random noise process.
12
References
Combining and updating of local estimates and regional maps along sets of one-dimensional tracks
TL;DR: In this article, the problem of combining and updating estimates that may have been generated in a distributed fashion or may represent estimates, generated at different times, of the same process sample path is considered.
Distributed estimation algorithms for nonlinear systems
TL;DR: A distributed processing algorithm is obtained which recovers exactly the centralized conditional distribution of a random variable which has been generated by local observers having access to their private information.
A scattering framework for decentralized estimation problems
TL;DR: This approach imbeds a decentralized estimation problem into an equivalent scattering problem, and makes use of the super-position principle to relate local and centralized estimates.
67
Smoothing error dynamics and their use in the solution of smoothing and mapping problems
TL;DR: Martingale decomposition techniques are used to derive Markovian models for the error in smoothed estimates of processes described by linear models driven by white noise, providing a simple unified framework for examining a variety of problems involving the efficient assimilation of spatial data, which are referred to as mapping problems.
21
Asymptotic agreement in distributed estimation
Vivek S. Borkar,Pravin Varaiya +1 more
TL;DR: A subset of agents forms a communicating ring if for every pair of ring members, there is a sequence of ring ring members such that each ring member knows that he is a ring member, and the estimates of all the ring members asymptotically agree as mentioned in this paper.
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