Probability for statistics and machine learning: fundamentals and advanced topics
By: Dasgupta, Anirban
Material type:
Item type | Current location | Item location | Call number | Status | Date due | Barcode |
---|---|---|---|---|---|---|
Books | Vikram Sarabhai Library | Slot 1395 (0 Floor, East Wing) | 519.1 D2P7 (Browse shelf) | Available | 174389 |
This book can be used as a text for a year long graduate course in statistics, computer science, or mathematics, for self-study, and as an invaluable research reference on probabiliity and its applications. Particularly worth mentioning are the treatments of distribution theory, asymptotics, simulation and Markov Chain Monte Carlo, Markov chains and martingales, Gaussian processes, VC theory, probability metrics, large deviations, bootstrap, the EM algorithm, confidence intervals, maximum likelihood and Bayes estimates, exponential families, kernels, and Hilbert spaces, and a self contained complete review of univariate probability. (http://www.springer.com/statistics/statistical+theory+and+methods/book/978-1-4419-9633-6)
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