Inverse reinforcement learning (IRL) is the problem of inferring the underlying reward function from the expert's behavior data. The difficulty in IRL mainly arises in choosing the best reward function since there are typically an infinite number of reward functions that yield the given behavior data as optimal. Another difficulty comes from the noisy behavior data due to sub-optimal experts. We propose a hierarchical Bayesian framework, which subsumes most of the previous IRL algorithms as well as models the sub-optimality of the expert's behavior. Using a number of experiments on a synthetic problem, we demonstrate the effectiveness of our approach including the robustness of our hierarchical Bayesian framework to the sub-optimal expert behavior data. Using a real dataset from taxi GPS traces, we additionally show that our approach predicts the driving behavior with a high accuracy.