We found a match
Your institution may have rights to this item. Sign in to continue.
- Title
Spatial modeling with system of stochastic partial differential equations.
- Authors
Hu, Xiangping; Steinsland, Ingelin
- Abstract
To define a spatial process as the solution to a stochastic partial differential equation ( SPDE) is an approach to spatial modeling that is gaining popularity. The model corresponds to a Gaussian random spatial process with Matérn covariance function. The SPDE approach allows for computational benefits and provides a framework for making valid complex models (e.g., nonstationary spatial models). Using systems of SPDEs to define spatial processes extends the class of models that can be specified as SPDEs, while the computational benefits are kept. In this study, we give an overview of the current state of spatial modeling with systems of SPDEs. Systems of SPDEs have contributed toward modeling and computational efficient inference for spatial Gaussian random field ( GRF) models with oscillating covariance functions and multivariate GRF models. For multivariate GRF models special systems of SPDEs corresponding to models known from the literature are set up. Little work has been done for exploring opportunities and properties of spatial processes defined as systems of SPDEs. We also describe some of the interesting topics for further research. WIREs Comput Stat 2016, 8:112-125. doi: 10.1002/wics.1378 For further resources related to this article, please visit the .
- Subjects
PARTIAL differential equations; ASYMPTOTIC theory in stochastic partial differential equations; NUMERICAL solutions to stochastic partial differential equations; GAUSSIAN processes; DISTRIBUTION (Probability theory)
- Publication
WIREs: Computational Statistics, 2016, Vol 8, Issue 2, p112
- ISSN
1939-5108
- Publication type
Article
- DOI
10.1002/wics.1378