Statistics

   

A Monte Carlo Scheme for Node-Specific Inference Over Wireless Sensor Networks

Authors: Luca Martino, Jorge Plata-Chaves, Francisco Louzada

In this work, we design an efficient Monte Carlo scheme for a node-specific inference problem where a vector of global parameters and multiple vectors of local parameters are involved. This scenario often appears in inference problems over heterogeneous wireless sensor networks where each node performs observations dependent on a vector of global parameters as well as a vector of local parameters. The proposed scheme uses parallel local MCMC chains and then an importance sampling (IS) fusion step that leverages all the observations of all the nodes when estimating the global parameters. The resulting algorithm is simple and flexible. It can be easily applied iteratively, or extended in a sequential framework.

Comments: 5 Pages.

Download: PDF

Submission history

[v1] 2016-03-11 17:50:17
[v2] 2016-03-12 06:01:27
[v3] 2016-03-13 11:19:11
[v4] 2016-03-14 15:52:37

Unique-IP document downloads: 68 times

Articles available on viXra.org are pre-prints that may not yet have been verified by peer-review and should therefore be treated as preliminary and speculative. Nothing stated should be treated as sound unless confirmed and endorsed by a concensus of independent qualified experts. In particular anything that appears to include financial or legal information or proposed medical treatments should not be taken as such. Vixra.org will not be responsible for any consequences of actions that result from any form of use of any documents on this website.

Add your own feedback and questions here:
You are equally welcome to be positive or negative about any paper but please be polite. If you are being critical you must mention at least one specific error, otherwise your comment will be deleted as unhelpful.

comments powered by Disqus