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.
|Number of pages||20|
|Journal||Communications in Statistics - Simulation and Computation|
|State||Published - 1 Jan 1994|
- posterior mean
- posterior variance
- prior distribution