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Preprint studies probability matching priors in the Gaussian AR(1) model, connecting various Bayesian methods.
This preprint studies probability matching priors in the stationary Gaussian AR(1) model. Using a Bayesian-route approach based on higher-order asymptotics, we connect Edgeworth expansions, posterior Laplace expansions, orthogonal nuisance parameters, and the Cox-Reid adjustment to characterize a matching-prior class for the autoregressive parameter. The record includes the manuscript, LaTeX source, simulation code, and simulation results.
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Xander Spruce (2026) studied this question.
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