Quantifying model uncertainty and performing model selection within a Bayesian framework is becoming an ever-larger part of scientific analysis both within and outside of astronomy. I will present a brief introduction to Nested Sampling, a complementary framework to Markov Chain Monte Carlo approaches that is designed to estimate marginal likelihoods (i.e. Bayesian evidences) and posterior distributions, outline some of their pros and cons, and briefly discuss more recent extensions such as Dynamic Nested Sampling. I will also briefly highlight `dynesty`, an open-source Python package designed to make it easy for researchers to applying Nested Sampling approaches to various “black box” likelihoods present in their work.