When doing statistical analysis, we often assume that data points are mutually independent. However, due to the way it was collected or the nature of the problem it came from, data is often structured into groups or clusters. In these situations, the assumption of independence comes into question. To tackle that, we introduce hierarchical models, where the probability distribution of data for each of the J groups is defined by the parameter θj and the relationship between these parameters is modeled by the joint distribution of θ1, . . . , θJ. Bayesian inference further broadens the use of hierarchical models because it enables us to make statistical conclusions about a certain group while incorporating information contained in the data belonging to other groups. In Bayesian hierarchical models, the joint distribution of θ1, . . . , θJ is defined by the hyperparameters ϕ, for which we also set a prior and calculate the posterior distribution using Bayesian inference. In this master’s thesis, we present the main theoretical results of Bayesian statistics, we discuss the motivation behind hierarchical modeling and give a detailed description of Bayesian hierarchical models, focusing on the procedure of setting up and analysing these models. We highlight the key advantages and principles of Bayesian hierarchical modeling using examples in which we also showcase computational methods for displaying results.