A constrained mixture of Normal-Inverse-Gamma models allows direct sampling of parameters given labels and uses preliminary estimators to restrict label space for feasible Bayesian inference without full MCMC.
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Bayesian Analysis Using a Constrained Mixture of Normal-Inverse-Gamma Models
A constrained mixture of Normal-Inverse-Gamma models allows direct sampling of parameters given labels and uses preliminary estimators to restrict label space for feasible Bayesian inference without full MCMC.