The Nested Logit Model is characterized by its assumption of correlation between alternatives and the hierarchical structuring of choices within the model. Using the Nested Logit Model allows for the measurement of utilities for choice sets that are difficult to observe directly, but it also presents challenges due to its multimodal likelihood function. As a result, the parameter estimates obtained using traditional maximum likelihood estimation methods may not always accurately reflect actual consumer choice behavior. This study proposes a method to improve the accuracy of parameter estimation by using the MCMC (Markov Chain Monte Carlo) method to estimate the parameters of the Nested Logit Model.