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Matérn cross-covariance functions for multivariate random fields
We introduce a flexible parametric family of matrix-valued covariance functions for
multivariate spatial random fields, where each constituent component is a Matérn process …
multivariate spatial random fields, where each constituent component is a Matérn process …
The dependent Dirichlet process and related models
Standard regression approaches assume that some finite number of the response
distribution characteristics, such as location and scale, change as a (parametric or …
distribution characteristics, such as location and scale, change as a (parametric or …
Geostatistical inference under preferential sampling
Geostatistics involves the fitting of spatially continuous models to spatially discrete data.
Preferential sampling arises when the process that determines the data locations and the …
Preferential sampling arises when the process that determines the data locations and the …
[ספר][B] Applied Bayesian hierarchical methods
PD Congdon - 2010 - taylorfrancis.com
The use of Markov chain Monte Carlo (MCMC) methods for estimating hierarchical models
involves complex data structures and is often described as a revolutionary development. An …
involves complex data structures and is often described as a revolutionary development. An …
Bayesian spatial quantile regression
Tropospheric ozone is one of the six criteria pollutants regulated by the United States
Environmental Protection Agency under the Clean Air Act and has been linked with several …
Environmental Protection Agency under the Clean Air Act and has been linked with several …
[ספר][B] Bayesian hierarchical models: with applications using R
PD Congdon - 2019 - taylorfrancis.com
An intermediate-level treatment of Bayesian hierarchical models and their applications, this
book demonstrates the advantages of a Bayesian approach to data sets involving inferences …
book demonstrates the advantages of a Bayesian approach to data sets involving inferences …
Nonparametric Bayes conditional distribution modeling with variable selection
This article considers a methodology for flexibly characterizing the relationship between a
response and multiple predictors. Goals are (1) to estimate the conditional response …
response and multiple predictors. Goals are (1) to estimate the conditional response …
Bayesian dependent mixture models: A predictive comparison and survey
Bayesian Dependent Mixture Models: A Predictive Comparison and Survey Page 1
Statistical Science 2025, Vol. 40, No. 1, 81–108 https://doi.org/10.1214/24-STS966 © …
Statistical Science 2025, Vol. 40, No. 1, 81–108 https://doi.org/10.1214/24-STS966 © …
Flexible Bayesian quantile regression for independent and clustered data
Quantile regression has emerged as a useful supplement to ordinary mean regression.
Traditional frequentist quantile regression makes very minimal assumptions on the form of …
Traditional frequentist quantile regression makes very minimal assumptions on the form of …
A review on Bayesian model-based clustering
Clustering is an important task in many areas of knowledge: medicine and epidemiology,
genomics, environmental science, economics, visual sciences, among others …
genomics, environmental science, economics, visual sciences, among others …