Reconstructing Probability Distributions from Data

Machine learning applications often assume that the observed data is sampled from probability distributions. How can these probability distributions be reverse engineered from observed data? The main challenge is that the data analyst only has access to observed data but no a priori knowledge of the shape of the underlying probability distribution.