Applications Of Bayesian Simulation Tools For Regression Modeling Of Agricultural Data

Research Article
Shagufta Yasmeen and Athar Ali Khan
Bayesian inference, Hamiltonian Monte Carlo, independent Metropolis, JAGS, Metropolis within Gibbs, R, sampling importance resampling, Stan.

A large part of applied statistical analysis is based on linear regression modeling technique which is one of the most widely used statistical tools in agriculture. These regression models are of particular interest of agriculturists for a variety of inferential tasks such as prediction, parameter estimation and data description. The theory of least squares is widely used to analyse the agricultural field experiments. In this paper an attempt has been made to implement parallel Bayesian methods of deterministic as well as simulation tools to regression models. Implementations have been made using R, JAGS and Stan packages.