Sang et al., 2011 - Google Patents
Covariance approximation for large multivariate spatial data sets with an application to multiple climate model errorsSang et al., 2011
View PDF- Document ID
- 10861595024694525467
- Author
- Sang H
- Jun M
- Huang J
- Publication year
- Publication venue
- The Annals of Applied Statistics
External Links
Snippet
This paper investigates the cross-correlations across multiple climate model errors. We build a Bayesian hierarchical model that accounts for the spatial dependence of individual models as well as cross-covariances across different climate models. Our method allows for a …
- 238000000034 method 0 abstract description 116
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/50—Computer-aided design
- G06F17/5009—Computer-aided design using simulation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
- G06K9/6232—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
- G06K9/6247—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on an approximation criterion, e.g. principal component analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS
- G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
- G01V1/28—Processing seismic data, e.g. analysis, for interpretation, for correction
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Sang et al. | Covariance approximation for large multivariate spatial data sets with an application to multiple climate model errors | |
| Murakami et al. | Random effects specifications in eigenvector spatial filtering: a simulation study | |
| Thibaud et al. | Threshold modeling of extreme spatial rainfall | |
| Su et al. | Beyond triple collocation: Applications to soil moisture monitoring | |
| CN117992757B (en) | Homeland ecological environment remote sensing data analysis method based on multidimensional data | |
| González‐Abad et al. | Using explainability to inform statistical downscaling based on deep learning beyond standard validation approaches | |
| Ray et al. | Bayesian calibration of the Community Land Model using surrogates | |
| Christensen et al. | Latent variable analysis of multivariate spatial data | |
| Yu et al. | Modeling spatial extremes via ensemble-of-trees of pairwise copulas | |
| Guhaniyogi et al. | Modeling complex spatial dependencies: Low-rank spatially varying cross-covariances with application to soil nutrient data | |
| Ming et al. | An advanced estimation algorithm for ground‐motion models with spatial correlation | |
| CN115248992A (en) | A spatiotemporal prediction method of ocean three-dimensional temperature and salinity based on compressed excitation PredRNN | |
| Hrafnkelsson et al. | Max-and-smooth: A two-step approach for approximate Bayesian inference in latent Gaussian models | |
| Cheng et al. | A graph clustering approach to localization for adaptive covariance tuning in data assimilation based on state-observation mapping | |
| Vu et al. | Modeling nonstationary and asymmetric multivariate spatial covariances via deformations | |
| Fix et al. | Simultaneous autoregressive models for spatial extremes | |
| Brasseur | Ocean data assimilation using sequential methods based on the Kalman filter | |
| Guhaniyogi et al. | Large multi-scale spatial modeling using tree shrinkage priors | |
| Kang et al. | A hierarchical model calibration approach with multiscale spectral-domain parameterization: Application to a structurally complex fractured reservoir | |
| Carey‐Smith et al. | A hidden seasonal switching model for multisite daily rainfall | |
| Morales et al. | Student’st process with spatial deformation for spatio-temporal data | |
| Zhang | Ensemble methods of data assimilation in porous media flow for non-gaussian prior probability density | |
| Sebacher et al. | Channelized reservoir estimation using a low-dimensional parameterization based on high-order singular value decomposition | |
| Bazargan et al. | Bayesian model selection for complex geological structures using polynomial chaos proxy | |
| Yarali et al. | Incorporating covariate information in the covariance structure of misaligned spatial data |