Neural Density Estimation and Likelihoodfree Inference
Abstract
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 likelihoodfree 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.
 Publication:

arXiv eprints
 Pub Date:
 October 2019
 arXiv:
 arXiv:1910.13233
 Bibcode:
 2019arXiv191013233P
 Keywords:

 Statistics  Machine Learning;
 Computer Science  Machine Learning
 EPrint:
 PhD thesis submitted to the University of Edinburgh in April 2019. Includes in full the following articles: arXiv:1605.06376, arXiv:1705.07057, arXiv:1805.07226