About Matthew Sainsbury-Dale Matthew Sainsbury-Dale Postdoctoral Research Fellow, Statistics spatio-temporal statistics Statistics of extremes machine learning Deep learning Matthew obtained his PhD in 2024 from the University of Wollongong (UOW), Australia, with a thesis titled "Efficient Inference for Spatial and Spatio-Temporal Statistical Models Using Basis-Function and Deep-Learning Methods", which received the Examiners Commendation for Outstanding Thesis and the Faculty of Engineering and Information Sciences Postgraduate Thesis Award. He is now a postdoctoral researcher at the King Abdullah University of Science and Technology (KAUST), working in the XSTAT group under Prof. Raphaël Huser's supervision. Education and Early Career Matthew obtained his PhD in 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].
