Deterministic objective Bayesian inference for spatial models [pdf]

Show HN: Deterministic objective Bayesian inference for spatial models [pdf]

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To give some context, objective Bayesian inference refers to Bayesian analysis (i.e. integrating over the parameter space) using a prior that is design to represent "minimal information" (see [1], [2], and [3] for an overview). Particularly in cases where a model's likelihood function is not strongly peaked about a point, objective Bayesian inference can give better results than methods based off of point estimates like Maximum Likelihood [4].Reference priors provides a general approach to construct so-called noninformative priors that are suitable for Objective Bayesian analysis ([5], [6]). The approach takes a practical viewpoint of noninformative priors and looks to build priors that are

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Website: buildingblock.ai/bayesian-gaussian-process.pdf