Abstract: In traditional statistical methods, the conjoint analysis method is can not estimate variables for a large number of parameters at the same time, therefore, a Bayesian 茁 regression model is proposed. In the newly established model, the Dirichlet distribution is used as the prior distribution of the model parameters, and the relevant MCMC(Markov Chain Monte Carlo) algorithm is designed to fit the model. By analyzing the results of applying the model to the evaluation of discrete index variables, it is shown that the model has a good fitting effect on the data and the algorithm has a fast convergence speed. It shows that the Bayesian hierarchical model makes up for the defects of the traditional conjoint analysis method, and optimizes and improves the conjoint analysis method.

Key words: Bayesian hierarchical model； , conjoint analysis； , Markov chain Monte Carlo(MCMC) algorithm； , Dirichlet distribution