1 - 7 of 7 Chapters
[Sensor networks are ubiquitous due to the recent technological breakthroughs in micro-electro-mechanical systems (MEMS), wireless communications, and embedded systems [9, 10].]
[Standard notation is used throughout this book.]
[We often assume that Gaussian processes are isotropic implying that the covariance function only depends on the distance between locations.]
[The main reason why the nonparametric prediction using Gaussian processes has not been popular for resource-constrained multi-agent systems is the fact that the optimal prediction must use all cumulatively measured values in a non-trivial way [74, 75].]
[In Chap. 4, we analyzed the conditions under which near-optimal prediction can be achieved using only truncated observations.]
[Recently, there have been efforts to find a way to fit a computationally efficient Gaussian Markov Random Field (GMRF) on a discrete lattice to a Gaussian random field on a continuum space [86–88].]
[In this chapter, we consider the problem of predicting a large scale spatial field using successive noisy measurements obtained by mobile sensing agents.]
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