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On Choosing and Bounding Probability Metrics
Open accessAbstract
Summary: When studying convergence of measures, an important issue is the choice of probability metric. We provide a summary and some new results concerning bounds among some important probability metrics/distances that are used by statisticians and probabilists. Knowledge of other metrics can provide a means of deriving bounds for another one in an applied problem. Considering other metrics can also provide alternate insights. We also give examples that show that rates of convergence can strongly depend on the metric chosen. Careful consideration is necessary when choosing a metric.
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Estimation of entropy and mutual information
Paninski, L. (2003) · Neural Computation · doi:10.1162/089976603321780272 · Cited by 1,442
Reinforcement learning of motor skills with policy gradients
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Agile Autonomous Driving using End-to-End Deep Imitation Learning
Pan, Y., Cheng, C.-A., Saigol, K., Lee, K., Yan, X., Theodorou, E., & Boots, B. (2018) · Robotics: Science and Systems XIV · doi:10.15607/rss.2018.xiv.056 · Cited by 257
Data Lifecycle Challenges in Production Machine Learning: A Survey
Polyzotis, N., Roy, S., Whang, S. E., & Zinkevich, M. (2018) · ACM SIGMOD Record · doi:10.1145/3299887.3299891 · Cited by 214
Convergence in distribution of random metric measure spaces (Λ-coalescent measure trees)
Greven, A., Pfaffelhuber, P., & Winter, A. (2009) · Probability Theory and Related Fields · doi:10.1007/s00440-008-0169-3 · Cited by 157
Metadata
- Authors
- Gibbs, A. L., & Su, F. E.
- Venue
- International Statistical Review
- Publisher
- Wiley
- Published
- 2002-12
- Volume
- 70
- Issue
- 3
- Pages
- 419-435
- ISSN
- 0306-7734 (ISSN-L)
- 1751-5823
- OpenAlex
- W2032916024
- Cited by
- 1,163
- References
- 49
- FWCI
- 8.64
- Type
- journal article
- Language
- en
- Open access
- bronze
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