Bayesian Analysis of Agricultural Experiments Using PROC MCM

The purpose of this study is to present the general concept Bayesian analysis and Markov chain Monte Carlo (MCMC) algorithm make some numerical comparisons with frequentist analyses. A factorial randomized complete-block (RCB) experiment used analyze cowpea data set that has four separate single-column replicates, each containing 9 combinations 3 varieties spacings. Response yield hay. Point estimates variance components obtained in under priors presented differences Restricted Maximum Likelihood (REML) estimate. method overestimates component compared REML agricultural experiments a very rich useful tool. It provides depth different features which are otherwise hidden cannot be explored using other techniques. Moreover, SAS software power efficiency deal as well graphical sets from experiments.