This module provides an introduction to Bayesian methods for biomedical research from basic principles to applications. Specifically, we discuss the Bayesian paradigm introducing the subjective interpretation of probability, Bayes Theorem, prior, posterior and predictive distributions and Markov chain Monte Carlo methods. We apply Bayesian methods to some biomedical problems including diagnostic testing, design and monitoring of clinical trials, meta-analyses among others. We use INLA, Jags and a number of R packages to illustrate the application of Bayesian methods.