About Matthew Sainsbury-Dale Matthew Sainsbury-Dale Research Scientist, Statistics amortized inference Deep learning machine learning simulation-based inference Spatial and spatio-temporal statistics Statistics of extremes statistical software Matthew Sainsbury-Dale's research focuses on statistical deep learning, simulation-based inference, spatial and spatio-temporal statistics, and the development of efficient statistical software. Articles Related News January 2024 Going likelihood-free with neural networks 2 min read · Tue, Jan 30 2024 News A neural network-based approach offers an extremely fast and highly accurate alternative to the "gold standard" of likelihood estimation for statistical modeling of complex datasets. July 2023 New paper accepted in The American Statistician 1 min read · Mon, Jul 31 2023 Spotlight News Bayes estimators Deep learning likelihood-free inference optimal inference spatial statistics New paper accepted: Sainsbury-Dale, M., Zammit-Mangion, A., and Huser, R. (2023+), Likelihood-free parameter estimation with neural Bayes estimators, The American Statistician, to appear [ PDF preprint].
Going likelihood-free with neural networks 2 min read · Tue, Jan 30 2024 News A neural network-based approach offers an extremely fast and highly accurate alternative to the "gold standard" of likelihood estimation for statistical modeling of complex datasets.
New paper accepted in The American Statistician 1 min read · Mon, Jul 31 2023 Spotlight News Bayes estimators Deep learning likelihood-free inference optimal inference spatial statistics New paper accepted: Sainsbury-Dale, M., Zammit-Mangion, A., and Huser, R. (2023+), Likelihood-free parameter estimation with neural Bayes estimators, The American Statistician, to appear [ PDF preprint].
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