著者: Alfred Müller - Advances in Applied Probability 1997 被引用: 550
We consider probability metrics of the following type: for a class of functions and probability measures P, Q we define A unified study of such integral probability metrics is given. We characterize the maximal class of functions that generates such a metric. Further, we show how some interesting properties of these probability metrics arise directly from conditions on the generating class of functions. The results are illustrated by several examples, including the Kolmogorov metric, the Dudley metric and the stop-loss metric.
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Real and Abstract Analysis Real and Abstract Analysis Topological Vector Spaces Topological Vector Spaces Lectures on analysis Lectures on analysis Convex Functions Convex Functions Normal Approximation and Asymptotic Expansions Normal Approximation and Asymptotic Expansions Probability Metrics Probability Metrics REAL ANALYSIS AND PROBABILITY REAL ANALYSIS AND PROBABILITY Real Analysis and Probability. Real Analysis and Probability. Probability Metrics and the Stability of… Probability Metrics and the Stability of Stochastic Models Probability Metrics and the Stability of… Probability Metrics and the Stability of Stochastic Models Normal Approximation and Asymptotic Expansions Normal Approximation and Asymptotic Expansions Topological Vector Spaces Topological Vector Spaces Lipschitz Continuity of Value Functions in… Lipschitz Continuity of Value Functions in Markovian Decision Processes Injective Hilbert Space Embeddings of… Injective Hilbert Space Embeddings of Probability Measures Equivalence of distance-based and… Equivalence of distance-based and RKHS-based statistics in hypothesis testing Stabilizing Training of Generative Adversarial… Stabilizing Training of Generative Adversarial Networks through Regularization Fisher GAN Fisher GAN On the Discrimination-Generali… On the Discrimination-Generalization Tradeoff in GANs Implicit Quantile Networks for… Implicit Quantile Networks for Distributional Reinforcement Learning Approximability of Discriminators Implies… Approximability of Discriminators Implies Diversity in GANs DeepMDP: Learning Continuous Latent Space… DeepMDP: Learning Continuous Latent Space Models for Representation Learning High-Dimensional Statistics: A… High-Dimensional Statistics: A Non-Asymptotic Viewpoint Generative Adversarial Networks (GANs)… Generative Adversarial Networks (GANs): Challenges, Solutions, and Future Directions Statistical Guarantees of Generative… Statistical Guarantees of Generative Adversarial Networks for Distribution Estimation Integral Probability Metrics and Their… Integral Probability Metrics and Their Generating Classes of Functions 過去の参考文献 中心の論文 この論文を引用する論文 古い 新しい ノードをクリックするとフォーカスを固定、空白をクリックすると本論文に戻ります。ホバーで一時的にプレビューできます。各ノードのページはタイトルから開けます。
