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Relevant bibliographies by topics / Spatio-temporal random fields

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Journal articles

Academic literature on the topic 'Spatio-temporal random fields'

Author: Grafiati

Published: 13 April 2024

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Journal articles on the topic "Spatio-temporal random fields"

1

Descombes, X., F. Kruggel, and D. Y. Von Cramon. "Spatio-temporal fMRI analysis using Markov random fields." IEEE Transactions on Medical Imaging 17, no. 6 (1998): 1028–39. http://dx.doi.org/10.1109/42.746636.

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2

De Iaco, S., and D. Posa. "Predicting spatio-temporal random fields: Some computational aspects." Computers & Geosciences 41 (April 2012): 12–24. http://dx.doi.org/10.1016/j.cageo.2011.11.014.

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Piatkowski, Nico, Sangkyun Lee, and Katharina Morik. "Spatio-temporal random fields: compressible representation and distributed estimation." Machine Learning 93, no. 1 (2013): 115–39. http://dx.doi.org/10.1007/s10994-013-5399-7.

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Ip, Ryan H. L., and W. K. Li. "Matérn cross-covariance functions for bivariate spatio-temporal random fields." Spatial Statistics 17 (August 2016): 22–37. http://dx.doi.org/10.1016/j.spasta.2016.04.004.

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Salvaña, Mary Lai O., and Marc G. Genton. "Nonstationary cross-covariance functions for multivariate spatio-temporal random fields." Spatial Statistics 37 (June 2020): 100411. http://dx.doi.org/10.1016/j.spasta.2020.100411.

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6

Fontanella, L., L. Ippoliti, R. J. Martin, and S. Trivisonno. "Interpolation of spatial and spatio-temporal Gaussian fields using Gaussian Markov random fields." Advances in Data Analysis and Classification 2, no. 1 (2008): 63–79. http://dx.doi.org/10.1007/s11634-008-0019-2.

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7

Das, Sonjoy, Roger Ghanem, and Steven Finette. "Polynomial chaos representation of spatio-temporal random fields from experimental measurements." Journal of Computational Physics 228, no. 23 (2009): 8726–51. http://dx.doi.org/10.1016/j.jcp.2009.08.025.

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8

Western, Luke M., Zhe Sha, Matthew Rigby, et al. "Bayesian spatio-temporal inference of trace gas emissions using an integrated nested Laplacian approximation and Gaussian Markov random fields." Geoscientific Model Development 13, no. 4 (2020): 2095–107. http://dx.doi.org/10.5194/gmd-13-2095-2020.

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Abstract. We present a method to infer spatially and spatio-temporally correlated emissions of greenhouse gases from atmospheric measurements and a chemical transport model. The method allows fast computation of spatial emissions using a hierarchical Bayesian framework as an alternative to Markov chain Monte Carlo algorithms. The spatial emissions follow a Gaussian process with a Matérn correlation structure which can be represented by a Gaussian Markov random field through a stochastic partial differential equation approach. The inference is based on an integrated nested Laplacian approximation (INLA) for hierarchical models with Gaussian latent fields. Combining an autoregressive temporal correlation and the Matérn field provides a full spatio-temporal correlation structure. We first demonstrate the method on a synthetic data example and follow this using a well-studied test case of inferring UK methane emissions from tall tower measurements of atmospheric mole fraction. Results from these two test cases show that this method can accurately estimate regional greenhouse gas emissions, accounting for spatio-temporal uncertainties that have traditionally been neglected in atmospheric inverse modelling.

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9

Jadaliha, Mahdi, Jinho Jeong, Yunfei Xu, Jongeun Choi, and Junghoon Kim. "Fully Bayesian Prediction Algorithms for Mobile Robotic Sensors under Uncertain Localization Using Gaussian Markov Random Fields." Sensors 18, no. 9 (2018): 2866. http://dx.doi.org/10.3390/s18092866.

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In this paper, we present algorithms for predicting a spatio-temporal random field measured by mobile robotic sensors under uncertainties in localization and measurements. The spatio-temporal field of interest is modeled by a sum of a time-varying mean function and a Gaussian Markov random field (GMRF) with unknown hyperparameters. We first derive the exact Bayesian solution to the problem of computing the predictive inference of the random field, taking into account observations, uncertain hyperparameters, measurement noise, and uncertain localization in a fully Bayesian point of view. We show that the exact solution for uncertain localization is not scalable as the number of observations increases. To cope with this exponentially increasing complexity and to be usable for mobile sensor networks with limited resources, we propose a scalable approximation with a controllable trade-off between approximation error and complexity to the exact solution. The effectiveness of the proposed algorithms is demonstrated by simulation and experimental results.

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10

Ghosh, Debraj, and Anup Suryawanshi. "Approximation of Spatio-Temporal Random Processes Using Tensor Decomposition." Communications in Computational Physics 16, no. 1 (2014): 75–95. http://dx.doi.org/10.4208/cicp.201112.191113a.

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AbstractA new representation of spatio-temporal random processes is proposed in this work. In practical applications, such processes are used to model velocity fields, temperature distributions, response of vibrating systems, to name a few. Finding an efficient representation for any random process leads to encapsulation of information which makes it more convenient for a practical implementations, for instance, in a computational mechanics problem. For a single-parameter process such as spatial or temporal process, the eigenvalue decomposition of the covariance matrix leads to the well-known Karhunen-Loève (KL) decomposition. However, for multiparameter processes such as a spatio-temporal process, the covariance function itself can be defined in multiple ways. Here the process is assumed to be measured at a finite set of spatial locations and a finite number of time instants. Then the spatial covariance matrix at different time instants are considered to define the covariance of the process. This set of square, symmetric, positive semi-definite matrices is then represented as a third-order tensor. A suitable decomposition of this tensor can identify the dominant components of the process, and these components are then used to define a closed-form representation of the process. The procedure is analogous to the KL decomposition for a single-parameter process, however, the decompositions and interpretations vary significantly. The tensor decompositions are successfully applied on (i) a heat conduction problem, (ii) a vibration problem, and (iii) a covariance function taken from the literature that was fitted to model a measured wind velocity data. It is observed that the proposed representation provides an efficient approximation to some processes. Furthermore, a comparison with KL decomposition showed that the proposed method is computationally cheaper than the KL, both in terms of computer memory and execution time.

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