Based on Bayesian theory and combined with Monte Carlo Markov chain algorithm, a secondary development of water quality hydrodynamic module of SWMM was carried out, and characteristic parameters such as node positions and infiltration flow time series of the pipeline network were deduced. Analyze the impact of random walk, uniform distribution, and normal distribution as suggested distributions on inversion accuracy, and improve the likelihood function of the Monte Carlo Markov chain algorithm under Bayesian theory. The results indicate that all three suggested distributions can converge under Bayesian theory. Uniform distribution and normal distribution have less dependence on the initial value of the algorithm, and the algorithm has good global traversal. Random walk will reduce the inversion accuracy due to local optima. The inversion process under the three suggested distributions all showed error accumulation. The modified Monte Carlo Markov chain algorithm can effectively avoid the accumulation of errors and improve the inversion accuracy of the time series variable.