Bayesian inference is considered when both the likelihood and the prior distributions are t-densities. Some efficient calculational algorithms in basic normal inference problems concerning the mean over a range of the prior parameters are compared. The algorithms discussed include an approximation via Taylor expansion, the Naylor-Smith algorithm, and the exact formulas developed earlier. Each of them has some drawbacks in terms of accuracy or speed. A combination for efficient calculation over a grid of the prior parameters is suggested.
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COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION
