Titlebook: Bayesian Prediction and Adaptive Sampling Algorithms for Mobile Sensor Networks; Online Environmental Yunfei Xu,Jongeun Choi,Tapabrata Mait

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Economic Theory and Human GeographyThe 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].

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https://doi.org/10.1007/978-981-32-9224-6Recently, 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].

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Volcanic Eruption and Human GeoscienceIn 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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Introduction,Sensor networks are ubiquitous due to the recent technological breakthroughs in micro-electro-mechanical systems (MEMS), wireless communications, and embedded systems [9, 10].

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Preliminaries,Standard notation is used throughout this book.

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Memory Efficient Prediction With Truncated Observations,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].

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