Bayesian Analysis of Agricultural Experiments Using PROC MCM

Yıl 2021, Cilt: 4 Sayı: 3, 88 - 96, 01.07.2021

Mehmet Ziya Fırat

https://doi.org/10.47115/bsagriculture.874580

Öz

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

Anahtar Kelimeler

Bayesian analysis, Agricultural experiments, Markov Chain Monte Carlo, PROC MCMC

Kaynakça

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