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Probability for statistics and machine learning: fundamentals and advanced topics

By: Dasgupta, Anirban.
Material type: materialTypeLabelBookSeries: Springer texts in statistics. Publisher: New York Springer 2011Description: xix, 782 p.ISBN: 9781441996336.Subject(s): Machine learning | Markov chain Monte CarloPanel data | Probabilities - Mathematical models | Stochastic processesDDC classification: 519.1 Summary: 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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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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