The article discusses using Monte Carlo methods, specifically Markov chain Monte Carlo, to estimate posterior quantities in complex Bayesian models. These methods help compute summaries of parameters when traditional analytical or numerical approaches fail. By generating samples from the posterior distribution, researchers can obtain accurate estimates of quantities of interest. Various basic Monte Carlo methods are explored, along with techniques to improve efficiency and control errors in simulation.