• Firdoos Yousuf
• Athar Ali Khan

The Normal model is very common and the most widely used model in the field of Statistics. In this paper, an endeavour has been made to fit the Bayesian inference procedures for the Normal model. The implementation is made through R, Stan and INLA software packages. INLA is based on approximating the posterior marginal distributions of the model parameters by means of different Laplace approximations. Codes that have been created in R and Stan to implement the Bayesian approach using both optimization and simulation tools. Stan is a high-level language written in a C++ library for Bayesian modelling. Real data sets are used for illustrations. Moreover, parallel simulation tools are also implemented and furthermore actualized with a broad utilization of rstan. Its comparison has been made with the implementation using R-INLA software package.

Bayesian Inference, Posterior, Simulation, RStan, R-INLA, R, HMC.

"Bayesian Analysis of Normal Model with Stan and Inla", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.406-415, June 2019, Available :http://www.jetir.org/papers/JETIR1907H64.pdf

"Bayesian Analysis of Normal Model with Stan and Inla", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp406-415, June 2019, Available at : http://www.jetir.org/papers/JETIR1907H64.pdf