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Matthew Sainsbury-Dale

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].

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Related Sites

  • Extreme Statistics (XSTAT)
  • Statistics (STAT)

Related Content

  • Articles
    2

Related Links

  • Julia software package NeuralEstimators.jl
  • Matthew Sainsbury-Dale on LinkedIn
  • Matthew Sainsbury-Dale on Google Scholar
  • Matthew Sainsbury-Dale on ResearchGate
  • Matthew Sainsbury-Dale on GitHub

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