Bayesian Geostatistics Using Predictive Stacking
Different Cholesky factor updates
Optimal stacking weights
Calculate distance matrix
Simulate spatial data on unit square
Univariate Bayesian spatial generalized linear model
Bayesian spatial generalized linear model using predictive stacking
Univariate Bayesian spatial linear model
Bayesian spatial linear model using predictive stacking
spStack: Bayesian Geostatistics Using Predictive Stacking
Sample from the stacked posterior distribution
Make a surface plot
Make two side-by-side surface plots
Fits Bayesian hierarchical spatial process models for point-referenced Gaussian, Poisson, binomial, and binary data using stacking of predictive densities. It involves sampling from analytically available posterior distributions conditional upon some candidate values of the spatial process parameters and, subsequently assimilate inference from these individual posterior distributions using Bayesian predictive stacking. Our algorithm is highly parallelizable and hence, much faster than traditional Markov chain Monte Carlo algorithms while delivering competitive predictive performance. See Zhang, Tang, and Banerjee (2024) <doi:10.48550/arXiv.2304.12414>, and, Pan, Zhang, Bradley, and Banerjee (2024) <doi:10.48550/arXiv.2406.04655> for details.
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