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Neural Density Estimation and Likelihood-free Inference
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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.
Forward citations
Cited by 5 Pith papers
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Position: The Future of Bayesian Prediction Is Prior-Fitted
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