Treballs Finals de Grau de Matemàtiques, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2017, Director: Olga Julià de Ferran[en] Bayesian statistics, based on Bayes' Theorem, arise when we expand classical inference to contexts where a more subjective interpretation of probability is needed. In this paper we define the basic concepts that are necessary to make bayesian inference. We are gonna focus on single-parameter models, as we will see: the binomial model, the normal model with known variance, the exponencial model and the Poisson model. We define concepts such as conjugate laws, informative and noninformative priors and other aspects that are useful for a bayesian analysis. Finally, one can nd two examples, both made using the statistic programming language R, where we apply most of the introduced concepts and techniques.