Analysis of agricultural experimental design by Bayesian methods

Experimental design and analysis is an invaluable tool in agricultural research as in engineering, medicine, natural sciences and social sciences. The advancements in Markov Chain Monte Carlo methods allowed Bayesian method which could be summarized as interpretation of posterior distributions which are obtained through likelihood function and prior distributions, to overcome complex models. The aim of this study is to give alternative solutions to randomized complete block design, factorial design, nested design and general linear mixed model which are commonly used in agricultural experimental designs by using Bayesian methods.