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pro vyhledávání: '"BEAUMONT, MARK"'
Estimating a distribution given access to its unnormalized density is pivotal in Bayesian inference, where the posterior is generally known only up to an unknown normalizing constant. Variational inference and Markov chain Monte Carlo methods are the
Externí odkaz:
http://arxiv.org/abs/2407.15687
Many machine learning problems can be seen as approximating a \textit{target} distribution using a \textit{particle} distribution by minimizing their statistical discrepancy. Wasserstein Gradient Flow can move particles along a path that minimizes th
Externí odkaz:
http://arxiv.org/abs/2305.15577
Computer simulations have proven a valuable tool for understanding complex phenomena across the sciences. However, the utility of simulators for modelling and forecasting purposes is often restricted by low data quality, as well as practical limits t
Externí odkaz:
http://arxiv.org/abs/2210.06564
We introduce Sequential Neural Posterior Score Estimation (SNPSE), a score-based method for Bayesian inference in simulator-based models. Our method, inspired by the remarkable success of score-based methods in generative modelling, leverages conditi
Externí odkaz:
http://arxiv.org/abs/2210.04872
Autor:
Beaumont, Mark, author
Publikováno v:
Arab Christians and the Qurʾan from the Origins of Islam to the Medieval Period. 35:83-105
