000 04081nam a22005055i 4500
001 978-1-4419-9634-3
003 DE-He213
005 20140220083729.0
007 cr nn 008mamaa
008 110517s2011 xxu| s |||| 0|eng d
020 _a9781441996343
_9978-1-4419-9634-3
024 7 _a10.1007/978-1-4419-9634-3
_2doi
050 4 _aQA276-280
072 7 _aPBT
_2bicssc
072 7 _aMAT029000
_2bisacsh
082 0 4 _a519.5
_223
100 1 _aDasGupta, Anirban.
_eauthor.
245 1 0 _aProbability for Statistics and Machine Learning
_h[electronic resource] :
_bFundamentals and Advanced Topics /
_cby Anirban DasGupta.
264 1 _aNew York, NY :
_bSpringer New York :
_bImprint: Springer,
_c2011.
300 _aXX, 784 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringer Texts in Statistics,
_x1431-875X
505 0 _aChapter 1. Review of Univariate Probability -- Chapter 2. Multivariate Discrete Distributions -- Chapter 3. Multidimensional Densities -- Chapter 4. Advance Distribution Theory -- Chapter 5. Multivariate Normal and Related Distributions -- Chapter 6. Finite Sample Theory of Order Statistics and Extremes -- Chapter 7. Essential Asymptotics and Applications -- Chapter 8. Characteristic Functions and Applications -- Chapter 9. Asymptotics of Extremes and Order Statistics -- Chapter 10. Markov Chains and Applications -- Chapter 11. Random Walks -- Chapter 12. Brownian Motion and Gaussian Processes -- Chapter 13. Posson Processes and Applications -- Chapter 14. Discrete Time Martingales and Concentration Inequalities -- Chapter 15. Probability Metrics -- Chapter 16. Empirical Processes and VC Theory -- Chapter 17. Large Deviations -- Chapter 18. The Exponential Family and Statistical Applications -- Chapter 19. Simulation and Markov Chain Monte Carlo -- Chapter 20. Useful Tools for Statistics and Machine Learning -- Appendix A. Symbols, Useful Formulas, and Normal Table.
520 _aThis book provides a versatile and lucid 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 is written in an extremely accessible style, with elaborate motivating discussions and numerous worked out examples and exercises. The book has 20 chapters on a wide range of topics, 423 worked out examples, and 808 exercises. It is unique in its unification of probability and statistics, its coverage and its superb exercise sets, detailed bibliography, and in its substantive treatment of many topics of current importance. 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.
650 0 _aStatistics.
650 0 _aComputer simulation.
650 0 _aBioinformatics.
650 0 _aDistribution (Probability theory).
650 0 _aMathematical statistics.
650 1 4 _aStatistics.
650 2 4 _aStatistical Theory and Methods.
650 2 4 _aProbability Theory and Stochastic Processes.
650 2 4 _aSimulation and Modeling.
650 2 4 _aBioinformatics.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9781441996336
830 0 _aSpringer Texts in Statistics,
_x1431-875X
856 4 0 _uhttp://dx.doi.org/10.1007/978-1-4419-9634-3
912 _aZDB-2-SMA
999 _c106088
_d106088