A Useful Convergence Theorem for Probability Distributions

By H. Scheffé. 1947

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Felix X. Yu
2. Ananda Theertha Suresh
3. Sanjiv Kumar
4. Jakub Konecný
5. Krzysztof Marcin Choromanski
6. Yuhan Liu
7. Thomas G. Dietterich
8. Michael D. Riley

Citations in the corpus

• Learning discrete distributions: user vs item-level privacy (2020, methodology): “It gives a score for every hypothesis using the Scheffé algorithm [Scheffé, 1947] and then privately selects a hypothesis using the exponential mechanism based on the score functions.”
• Adaptive computation and machine learning (1998, background): “Another important theorem we need is the following as found in Scheffé (1947).”
• On the asymptotic properties of a nonparametric L/sub 1/-test statistic of homogeneity (2005, unknown): “Indeed, by Scheffé’s [9] theorem for partitions Ln = A2P j n(A) (A)j = 2 max A2 (P ) ( n(A) (A)) :”