Papers › Deterministic Objective Bayesian Analysis for Spatial Models
Deterministic Objective Bayesian Analysis for Spatial Models
Ryan Burn
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Berger et al. (2001) and Ren et al. (2012) derived noninformative priors for Gaussian process models of spatially correlated data using the reference prior approach (Berger, Bernardo, 1991). The priors have good statistical properties and provide a basis for objective Bayesian analysis (Berger, 2006). Using a trust-region algorithm for optimization with exact equations for posterior derivatives and an adaptive sparse grid at Chebyshev nodes, this paper develops deterministic algorithms for fully Bayesian prediction and inference with the priors. Implementations of the algorithms are available at https://github.com/rnburn/bbai.
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