Neural Density Estimation and Likelihood-free Inference
Machine Learning
2019-10-30 v1 Machine Learning
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 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.
Cite
@article{arxiv.1910.13233,
title = {Neural Density Estimation and Likelihood-free Inference},
author = {George Papamakarios},
journal= {arXiv preprint arXiv:1910.13233},
year = {2019}
}
Comments
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