Bayesian Prediction and Adaptive Sampling Algorithms for Mobile Sensor Networks: Online Environmental Field Reconstruction in Space and Time 1st Edition Yunfei Xu 3319219219 9783319219219

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Product details:

• ISBN 10: 3319219219
• ISBN 13: 9783319219219
• Author: Yunfei Xu 

This brief introduces a class of problems and models for the prediction of the scalar field of interest from noisy observations collected by mobile sensor networks. It also introduces the problem of optimal coordination of robotic sensors to maximize the prediction quality subject to communication and mobility constraints either in a centralized or distributed manner. To solve such problems, fully Bayesian approaches are adopted, allowing various sources of uncertainties to be integrated into an inferential framework effectively capturing all aspects of variability involved. The fully Bayesian approach also allows the most appropriate values for additional model parameters to be selected automatically by data, and the optimal inference and prediction for the underlying scalar field to be achieved.

Table of contents:

1 Introduction

2 Preliminaries

3 Learning Covariance Functions

4 Memory Efficient Prediction With Truncated Observations

5 Fully Bayesian Approach

6 New Efficient Spatial Model with Built-In Gaussian Markov Random Fields

7 Fully Bayesian Spatial Prediction Using Gaussian Markov Random Fields

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