The Methods of Distances in the Theory of Probability and Statistics

By Svetlozar T. Rachev, L. Klebanov, Stoyan Stoyanov, Frank J Fabozzi 3. 2013

196 citations recorded by the source corpus.

Subfields: Signal Processing

View Paper: https://www.semanticscholar.org/paper/1fd8fecde99bdaffde239ad6fe62aa1fc1696ef2

How researchers see this paper

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

In Signal Processing

1. Balaji Lakshminarayanan
2. Shakir Mohamed
3. Clara Meister
4. R´emi Munos
5. Ryan Cotterell
6. Marc G. Bellemare
7. Ivo Danihelka
8. Will Dabney

Citations in the corpus

• Language Model Evaluation Beyond Perplexity (2021, methodology): “To this end, we propose the use of several probability metrics (Mostafaei and Kord-nourie, 2011; Rachev et al., 2013) as our notion of statistical distance.”
• Generalized optimal sub-pattern assignment metric (2016, methodology): “If we have a metric in R N , we have a metric on random vectors in R N by taking the expected value [14, Sec.”
• The Cramer Distance as a Solution to Biased Wasserstein Gradients (2017, background): “Then This is a special case of the celebrated Monge-Kantorovich duality (Rachev et al., 2013), and is the integral probability metric (IPM) with function class F ∞ (Müller, 1997).”