Neural Density Estimation and Likelihood-free Inference
This work addresses fundamental problems in machine learning and statistics, but it appears incremental as it builds on existing neural network advances without specifying novel breakthroughs.
The thesis tackled density estimation and likelihood-free inference by developing new methods based on neural networks and deep learning, but no concrete results or numbers were provided.
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.