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Bayesian methods have become very popular in molecular phylogenetics due to the availability of user-friendly software for running sophisticated models of evolution. However, Bayesian phylogenetic models are complex, and analyses are often carried out using default settings, which may not be appropriate. Here we summarize the major features of Bayesian phylogenetic inference and discuss Bayesian computation using Markov chain Monte Carlo (MCMC) sampling, the diagnosis of an MCMC run, and ways of summarizing the MCMC sample. We discuss the specification of the prior, the choice of the substitution model and partitioning of the data. Finally, we provide a list of common Bayesian phylogenetic software packages and recommend appropriate applications.