This chapter describes how Bayesian inference is applied in Hierarchical Modelling of Species Communities (HMSC). The chapter starts by summarising the structure of the core HMSC model. It then briefly recalls some of the fundamentals of Bayesian inference, aimed primarily for those readers who are not very familiar with it. The core part of the chapter describes the structure of the prior distribution of HMSC and explains in particular how the default prior has been chosen. The chapter also briefly discusses how posterior sampling is conducted in HMSC through Markov chain Monte Carlo. The chapter uses the R-package HMSC-R to demonstrate how the prior distribution can be sampled, and to illustrate that samples from the prior distribution are identical to posterior samples if the model does not have any data. Finally, the chapter discusses how the computational time needed to fit an HMSC model depends on the size and type of the data.