Bayesian prediction of clipped Gaussian random fields

Computational Statistics and Data Analysis

Volumn 34, Issue 3, 2000, Pages 299-314

  De Oliveira, Victor ^a  

^a UNIVERSIDAD SIMÓN BOLÍVAR   (Venezuela)

Author keywords

Binary map; Binary random field; Data augmentation; Indicator kriging; Semivariogram; Spatial prediction

Indexed keywords

MATHEMATICAL MODELS; MONTE CARLO METHODS; OPTIMIZATION; PROBABILITY;

BAYESIAN METHOD; BINARY RANDOM FIELD; DATA AUGMENTATION; GAUSSIAN RANDOM FIELD;

MARKOV PROCESSES;

BAYESIAN ANALYSIS;

EID: 0034282333     PISSN: 01679473     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0167-9473(99)00103-6     Document Type: Article

References (26)

1. Bayesian analysis of binary and polychotomous response data
2. Multivariate probit analysis
3. Monte Carlo bayesian methods for discrete regression models and categorical time series
4. Analysis of multivariate probit models
5. Understanding the Metropolis-Hastings algorithm
6. Prediction diagnostics for spatial linear models
7. Model-based geostatistics (with discussion)
8. The estimation from individual records of the relationship between dose and quantal response
9. On bias, variance, 0/1-loss, and the curse-of-dimensionality
10. Efficient metropolis jumping rules
11. Efficient simulation from the multivariate normal and student-t distributions subject to linear constraints
12. Assessing the risk of soil contamination in the Swiss jura using indicator geostatistics
13. A composite likelihood approach to binary spatial data
14. Nonparametric estimation of spatial distributions
15. Parameter estimation for excursion set texture models
16. Likelihood inference in a correlated probit regression model
17. Mapping by simple indicator kriging
18. Estimation of spatial distributions from point sources with application to air pollution measurement