A new construction of covariance functions for Gaussian random fields
From MaRDI portal
Publication:6123502
DOI10.1007/s13171-023-00336-4OpenAlexW4390918438WikidataQ129881294 ScholiaQ129881294MaRDI QIDQ6123502
Athanasios C. Micheas, Weichao Wu
Publication date: 4 March 2024
Published in: Sankhyā. Series A (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s13171-023-00336-4
Computational methods for problems pertaining to statistics (62-08) Exact distribution theory in statistics (62E15) Characteristic functions; other transforms (60E10)
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- An Explicit Link between Gaussian Fields and Gaussian Markov Random Fields: The Stochastic Partial Differential Equation Approach
- Spatial variation. 2nd ed
- On approximations of the beta process in latent feature models: point processes approach
- Ancestral graph Markov models.
- Spatial statistics and computational methods
- Area-interaction point processes
- A criterion for local model selection
- Selection of a covariance function for a Gaussian random field aimed for modeling global optimization problems
- Likelihood Inference for Unions of Interacting Discs
- MODELS FOR TWO-DIMENSIONAL STATIONARY STOCHASTIC PROCESSES
- A general class of models for stationary two-dimensional random processes
- A model for clustering
- The intrinsic random functions and their applications
- Markov Point Processes and Their Applications
- Classes of Nonseparable, Spatio-Temporal Stationary Covariance Functions
- Hierarchical Bayesian modeling of marked non-homogeneous Poisson processes with finite mixtures and inclusion of covariate information
- Topographic correlation, power-law covariance functions, and diffusion
- Space–Time Covariance Functions
- Mass transport in water waves
- ON STATIONARY PROCESSES IN THE PLANE
- Graphical models
- Cox Point Processes: Why One Realisation Is Not Enough