UNS Conference Portal, The 1st International Conference on Science, Mathematics, Environment and Education 2017

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Parameters Estimation of Multivariate Bayesian Model using Informativer Prior
Dina Ariek Prasdika

Last modified: 2017-06-22


Multivariate regression model is a statistical method used to relate dependent variables to a set of independent variables. Bayesian method is used for parameter estimation multivariate regression model. There are two distributions in the Bayesian method, prior distribution and posterior distribution.  If informasion of parameters has known then it be called informative  prior. If the posterior distribution parameter is very complicated and can not be resolved directly, then it can be resolved with approaching posterior distribution parameter with Marcov chain Monte Carlo (MCMC) method. The purpose of this research is to estimate the parameter of multivariate regression model using Bayesian method. The results of estimation parameter for multivariate regression model are obtained after the simulation and are applied to the case. Estimation of parameter is done by determined prior and posterior distribution, then made simulation with Gibbs sampling algorithm. Prior distribution of  with and  Posterior distribution of multivariate regression model can be written by with  and . The posterior distribution parameter of multivariate regression model is approached with Gibbs sampling algorithm.