  I consider two problems in machine learning and statistics: the problem of
estimating the joint probability density of a collection of random variables,
known as density estimation, and the problem of inferring model parameters when
their likelihood is intractable, known as likelihood-free inference. The
contribution of the thesis is a set of new methods for addressing these
problems that are based on recent advances in neural networks and deep
learning.
