2期

最新文章

• Gesine Reinert's contribution to the Discussion of 'Statistical exploration of the Manifold Hypothesis' by Whiteley et al. AI翻译
  作者：Reinert, Gesine
  作者单位：University of Oxford
• Alberto Bordino and Olga Klopp's contribution to the Discussion of 'Statistical exploration of the Manifold Hypothesis' by Whiteley et al. AI翻译
  作者：Bordino, Alberto; Klopp, Olga
  作者单位：University of Warwick; ESSEC Business School
• Milan Stehlík and Martin Schlather's contribution to the Discussion of 'Statistical exploration of the manifold hypothesis' by Whiteley et al. AI翻译
  作者：Stehlik, Milan; Schlather, Martin
  作者单位：Universidad de Valparaiso; University of Mannheim
• Ian Gallagher's contribution to the Discussion of 'Statistical exploration of the manifold hypothesis' by Whiteley et al. AI翻译
  作者：Gallagher, Ian
  作者单位：University of Melbourne
• Maozai Tian, Shaopei Ma, Zhen Yu, and Yanan Hu's contribution to the Discussion of 'Statistical exploration of the manifold hypothesis' by Whiteley et al. AI翻译
  作者：Tian, Maozai; Ma, Shaopei; Yu, Zhen; Hu, Yanan
  作者单位：Renmin University of China; Xinjiang University of Finance & Economics; Changji University; University of International Business & Economics; University of International Business & Economics; Zhengzhou University
• Inference of dependency knowledge graph for Electronic Health Records (Sep, qkaf061, 2025) AI翻译
  作者：Xu, Zhiwei; Gan, Ziming; Zhou, Doudou; Shen, Shuting; Lu, Junwei; Cai, Tianxi
• Representation of context-specific causal models with observational and interventional data AI翻译
  作者：Duarte, Eliana; Solus, Liam
  作者单位：Universidade do Porto; Royal Institute of Technology
  摘要：We address the problem of representing context-specific causal models based on both observational and experimental data collected under general (e.g. hard or soft) interventions by introducing a new family of context-specific conditional independence models called CStrees. This family is defined via a novel factorization criterion that allows for a generalization of the factorization property defining general interventional directed acyclic graph (DAG) models. We derive a graphical characteriz...
• Bootstrapping estimators based on the block maxima method AI翻译
  作者：Bucher, Axel; Staud, Torben
  作者单位：Ruhr University Bochum
  摘要：The block maxima method is a standard approach for analyzing the extremal behaviour of a potentially multivariate time series. It has recently been found that the classical approach based on disjoint block maxima may be universally improved by considering sliding block maxima instead. However, the asymptotic variance formula for estimators based on sliding block maxima involves an integral over the covariance of a certain family of multivariate extreme value distributions, which makes its esti...
• Statistical exploration of the manifold hypothesis AI翻译
  作者：Agterberg, Joshua
  作者单位：University of Illinois System; University of Illinois Urbana-Champaign
  摘要：The manifold hypothesis is a widely accepted tenet of machine learning which asserts that nominally high-dimensional data are in fact concentrated near a low-dimensional manifold, embedded in high-dimensional space. This phenomenon is observed empirically in many real-world situations, has led to development of a wide range of statistical methods in the last few decades, and has been suggested as a key factor in the success of modern AI technologies. We show that rich and sometimes intricate m...
• Andrew Gelman's contribution to the Discussion of 'Statistical exploration of the Manifold Hypothesis' by Whiteley et al. AI翻译
  作者：Gelman, Andrew
  作者单位：Columbia University; Columbia University