In the Bayesian estimation framework, the parameters we want to estimate are considered to be random and every prior information we may know about them is modeled as a probability distribution function referred as prior distribution. The collected data also have information about these parameters and are represented by the likelihood distribution. Bayesian estimation performs a trade-off between both sources of information by calculating a new probability distribution called posterior distribution which is the update of our prior distribution using the data (likelihood).