Neural Density Estimation and Likelihood-free Inference
arXiv:1910.13233
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.
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
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