The Use of Approximate Prior Distributions in a Bayesian Decision Model
[Consider a Bayesian decision problem in which F is the prior distribution over some parameter space T. If —ψ(d, t) is the product of the loss function and the likelihood function, then the Bayesian solution, d_F, maximizes extlesstex-math extgreater$E_\F\(d)= extbackslashint _\T\ extbackslashpsi (d,t)dF(t)$ extless/tex-math extgreater. Suppose \F^n\ is a sequence of distribution functions that approach F^0 in the sup-metric topology. Our main theorem gives conditions under which extlesstex-math extgreater$d_\F extasciicircum \\ extbackslashrightarrow d_\F extasciicircum\0$ extless/tex-math extgreater and extlesstex-math extgreater$E_\F extasciicircum\0\\(d_\F extasciicircum \\) extbackslashrightarrow E_\F extasciicircum\0\\(d_\F extasciicircum\0\\)$ extless/tex-math extgreater.]