Approximating Bayesian Posterior Means Using Multivariate Gaussian Quadrature
نویسندگان
چکیده
Multiple integrals encountered when evaluating posterior densities in Bayesian estimation were approximated using Multivariate Gaussian Quadrature (MGQ). Experimental results for a linear regression model suggest MGQ provides better approximations to unknown parameters and error variance than simple Monte Carlo based approximations. The authors are Graduate Research Assistant, Professor and Graduate Research Assistant respectively, in the Department of Agricultural Economics, Purdue University, West Lafayette, Indiana, 47907-1145. Approximating Bayesian Posterior Means using Multivariate Gaussian Quadrature Abstract Multiple integrals encountered when evaluating posterior densities in Bayesian estimation were approximated using Multivariate Gaussian Quadrature (MGQ). Experimental results for a linear regression model suggest MGQ provides better approximations to unknown parameters and error variance than simple Monte Carlo based approximations.
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