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About this product
- DescriptionThis accessible book provides a versatile treatment of classic as well as modern probability theory, while integrating them with core topics in statistical theory and also some key tools in machine learning. It contains many worked out examples and exercises.
- Author BiographyAnirban DasGupta has been professor of statistics at Purdue University since 1994. He is the author of Springer's Asymptotic Theory of Probability and Statistics, and Fundamentals of Probability, A First Course. He is an associate editor of the Annals of Statistics and has also served on the editorial boards of JASA, Journal of Statistical Planning and Inference, International Statistical Review, Statistics Surveys, Sankhya, and Metrika. He has edited four research monographs, and has recently edited the selected works of Debabrata Basu. He was elected a Fellow of the IMS in 1993, is a former member of the IMS Council, and has authored a total of 105 monographs and research articles.
- Author(s)Anirban DasGupta
- PublisherSpringer-Verlag New York Inc.
- Date of Publication14/07/2013
- GenreComputing: Professional & Programming
- Series TitleSpringer Texts in Statistics
- Place of PublicationNew York, NY
- Country of PublicationUnited States
- ImprintSpringer-Verlag New York Inc.
- Content Notebiography
- Weight1211 g
- Width155 mm
- Height235 mm
- Spine42 mm
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