This chapter illustrates applications of McMC in a Bayesian context. The treatment is mostly schematic; the objective is to present the mechanics of McMC in different modelling scenarios. Many of the examples discussed in connection with implementation of maximum likelihood (using Newton-Raphson and EM), are revisited from a Bayesian McMC perspective. These include the analysis of ABO blood group data, binary regression, the genomic model, the two-component mixture model and the Bayesian analysis of truncated data. Further examples are discussed in the second part of the book on Prediction and in the Exercise sections, including their solutions, at the end of the book.