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Stats, Volume 8, Issue 2
June 2025 - 24 articles
Cover Story: Automatic differentiation (AD) is a key tool in modern statistical computing, enabling efficient and accurate gradient computation for tasks such as parameter estimation, sensitivity analysis, and simulation-based inference. In this work, we revisit AD from first principles and develop a vectorised formulation based on matrix calculus, tailored to the linear algebra conventions common in statistics. This approach mirrors analytical derivations, supports high-level optimisation techniques, and facilitates seamless integration with statistical modelling. By matching the structure of statistical modelling, our method enhances the applicability and clarity of AD. The days of painstakingly deriving gradients by hand are behind us, and now we can focus on building models, not derivatives. View this paper
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Stats - ISSN 2571-905X