Métodos Bayesianos

The Bayesian paradigm is an important option in statistics by its flexibility in handling complex problems and its corresponding easiness of common interpretation of statistical conclusions. Its use has been potentiated by the huge computational development. The main objective of this CU is to introduce the Bayesian statistics approach. By the end of this CU, a student must understand the principles that rules Bayesian inference, know how to incorporate, in various problems, the existing prior knowledge and its corresponding uncertainty in a probability distribution, know how to update the prior distribution with data to estimate analytically or numerically the resulting posterior probability distribution, through intensive programming methods as Markov Chain Monte Carlo (MCMC) and know how to use hierarchical modelling to represent and analyze complex systems, using the software R-project and BUGS (run in R).