We often deal with uncertain events for which no probabilities are known. According to the standard model of decision making under uncertainty, subjective expected utility, people form subjective beliefs about the likelihood of different outcomes and combine these beliefs with their preferences to reach a decision. Hence, being able to measure beliefs over ambiguous events is important in many economic applications. Nevertheless, beliefs have been relatively neglected in the literature, which has mostly focused on the preference component. Beliefs thus remain understudied and provide a fertile ground for future investigations. In this research project, I aim to fill this gap and systematically develop simple tools that allow to efficiently measure subjective belief distributions. First, I will theoretically examine existing approaches that have been used to measure beliefs over uncertain events in order to assess their viability in applications. Subsequently, I will develop a toolkit to elicit belief distributions, combining theoretically sound methods with simple and robust measurement tools and an efficient econometric setup. After a theoretical and simulation-based study, I will test the methodology empirically in a laboratory experiment. This will be followed by a field study, in which I will apply the toolkit to investigate beliefs about climate change in a large representative subject pool of a major European country.