It is well known that survival models are generally quite hard to fit. Bayesian paradigm in survival analysis greatly eases semiparametric models, especially in biomedicine where the semiparametic models, that are evaluated in this paper, are mostly used. For each of the models in this paper, we presented ways to reach a priori, we performed the likelihood function and the posterior distribution. Gibbs sampler, which is one of the MCMC sampling algorithms, plays an important role in the Bayesian paradigm in survival analysis. It allows us to obtain the posterior distributions for the model. The great advantage of Bayesian paradigm is that it allows us to draw some assumptions from earlier research and, if they are similar, use them for the model of the new research. This can be seen in clinical studies where, for example, researchers study variety of diseases such as cancer, AIDS and similar. However, there are some disadvantages to this approach. The greatest flaw is that it is based on the statistician’s subjectivity. If the wrong prior distribution is chosen, wrong results can occur. Bayesian paradigm in survival analysis can be a powerful tool, especially when there is good software available that eases the implementation of this models. However, it is required to know how to use it properly.