Likelihood maximization and entropy maximization are two common techniques used to infer the set of parameters of a probability distribution. In recent years, they have shown outstanding performance in inference problems of structural biology from sequence data. My work addresses two main aspects related to this subject. The first one is the prediction of contacts in a protein family through the analysis of correlation between residues. Standard information theory related methods based on local correlation measures (e.g. Mutual Information) that are routinely used to evaluate the correlation between two random variables, often fail because they are not able to disentangle direct from indirect interaction between variables.