Spatial Sensor Selection via Gaussian Markov Random Fields

Subset Selection for Gaussian Markov Random Fields

Given a Gaussian Markov random field, we consider the problem of selecting a subset of variables to observe which minimizes the total expected squared prediction error of the unobserved variables. We first show that finding an exact solution is NP-hard even for a restricted class of Gaussian Markov random fields, called Gaussian free fields, which arise in semi-supervised learning and computer ...

Spatial prediction with mobile sensor networks using Gaussian processes with built-in Gaussian Markov random fields

In this paper, a new class of Gaussian processes is proposed for resource-constrained mobile sensor networks. Such a Gaussian process builds on a GMRFwith respect to a proximity graph over a surveillance region. The main advantages of using this class of Gaussian processes over standard Gaussian processes defined by mean and covariance functions are its numerical efficiency and scalability due ...

Transformed Gaussian Markov Random Fields and 1 Spatial Modeling

15 The Gaussian random field (GRF) and the Gaussian Markov random field (GMRF) have 16 been widely used to accommodate spatial dependence under the generalized linear mixed 17 model framework. These models have limitations rooted in the symmetry and thin tail of the 18 Gaussian distribution. We introduce a new class of random fields, termed transformed GRF 19 (TGRF), and a new class of Markov r...

Approximating Hidden Gaussian Markov Random Fields

This paper discusses how to construct approximations to a unimodal hidden Gaussian Markov random field on a graph of dimensionnwhen the likelihood consists of mutually independent data. We demonstrate that a class of non-Gaussian approximations can be constructed for a wide range of likelihood models. They have the appealing properties that exact samples can be drawn from them, the normalisatio...

Gaussian Markov Random Fields: Theory and Applications