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Probability: Theory and Examples
Abstract
This classic introduction to probability theory for beginning graduate students covers laws of large numbers, central limit theorems, random walks, martingales, Markov chains, ergodic theorems, and Brownian motion. It is a comprehensive treatment concentrating on the results that are the most useful for applications. Its philosophy is that the best way to learn probability is to see it in action, so there are 200 examples and 450 problems. The fourth edition begins with a short chapter on measure theory to orient readers new to the subject.
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Cited by
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Detecting Novel Associations in Large Data Sets
Reshef, D. N., Reshef, Y. A., Finucane, H. K., Grossman, S. R., McVean, G., Turnbaugh, P. J., Lander, E. S., Mitzenmacher, M., & Sabeti, P. C. (2011) · Science · doi:10.1126/science.1205438 · Cited by 3,339
Differentially private average consensus: Obstructions, trade-offs, and optimal algorithm design
Nozari, E., Tallapragada, P., & Cortés, J. (2017) · Automatica · doi:10.1016/j.automatica.2017.03.016 · Cited by 334
General Forms of Finite Population Central Limit Theorems with Applications to Causal Inference
Li, X., & Ding, P. (2017) · Journal of the American Statistical Association · doi:10.1080/01621459.2017.1295865 · Cited by 167
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Metadata
- Authors
- Durrett, R.
- Venue
- Cambridge University Press eBooks
- Publisher
- Cambridge University Press
- Published
- 2010-08-30
- OpenAlex
- W4232753567
- Cited by
- 1,251
- References
- 0
- FWCI
- 11.76
- Type
- book
- Language
- en
- Open access
- closed
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
