Abstract
In this thesis, we discuss a generative model for Bayesian image analysis. In this model, we focus on building a prior of pares of an image with context information based on compositionality and a conditional model of image pixels given a particular interpretation. Also, a MCMC inference algorithm, Gibbs sampler, is introduced. Finally, Gibbs sampler and our model will be applied to a facial pose estimation experiment.