Bayesian Inference in Random Coefficient Linear Models |
Common terms and phrases
Amount annuities assumed assumptions B₁ bimodal calculate canonical variables Chapter coefficients components computation conditional Consider continued correlations death defined definite density derivation determinant diagonalization Dickey discuss equal equations errors example expressed factor force formulas function given hierarchical model hyperparameters individual inference inverted iterative Joint mode least squares estimates Lindley and Smith linear marginal distribution Marginal mode marginal posterior matrix mean method modal-type estimates moments multiple multivariate negative normal Note Number Number of policies numerical integration O'Hagan estimates obtain ordinary parameters performed possible posterior density posterior distribution posterior mean precision prior prior distribution problem procedure produced proposed random reason regression requires respect second stage shows simultaneous situation standardized suggest Table terminated Tiao transformation variables variance vector zero