The article shows that using Gibbs sampling can help estimate the marginal densities of complex distributions. By combining Gibbs sampling with Monte Carlo integration and conditional probability formulas, the researchers found that the estimated marginal densities closely match the true values in most cases. This method can be useful for analyzing multidimensional data and understanding the underlying patterns in the data.