For the problem of parameter estimation in generalized nonlinear models，a method of Bayesian estimation is proposed to extract the observations from the conditional posterior distribution of the parameters to estimate the parameters. In the Bayesian statistical analysis，the hybrid algorithm of the M-H algorithm and the Gibbs sampling algorithm in the Monte Carlo sampling method is used to analyze the model. The parameters values are extracted through the conditional posterior distribution of the parameters at each iteration，and the convergence of the Markov chain at iteration is verified by using the sample path figure and the mean traverse figure of the parameters. The Bayes estimation of parameter is obtained by calculating the posterior mean value of the Markov chain after the chain achieves convergence. Through the empirical analysis of product sales data，the biases of Bayesian estimation and Maximum Likelihood estimation are compared to verify the simplicity，validity，and feasibility of the M-H algorithm and the Gibbs sampling algorithm for Bayesian estimation of the parameters of the generalized nonlinear model.