03895nam a22005415i 4500001001800000003000900018005001700027007001500044008004100059020001800100024003500118050001900153050001600172072001600188072001700204072002300221082001400244100002500258245008200283264004600365300003300411336002600444337002600470338003600496347002400532490004500556505019500601520198000796650001702776650001502793650002902808650001902837650001902856650001602875650002802891650001702919650004902936650002802985650002703013650003603040650005203076650004203128710003403170773002003204776003603224830004503260856004803305978-1-4419-0162-0DE-He21320180115171454.0cr nn 008mamaa100301s2009 xxu| s |||| 0|eng d a97814419016207 a10.1007/978-1-4419-0162-02doi 4aQA273.A1-274.9 4aQA274-274.9 7aPBT2bicssc 7aPBWL2bicssc 7aMAT0290002bisacsh04a519.22231 aGut, Allan.eauthor.13aAn Intermediate Course in Probabilityh[electronic resource] /cby Allan Gut. 1aNew York, NY :bSpringer New York,c2009. aXV, 303 p.bonline resource. atextbtxt2rdacontent acomputerbc2rdamedia aonline resourcebcr2rdacarrier atext filebPDF2rda1 aSpringer Texts in Statistics,x1431-875X0 aMultivariate Random Variables -- Conditioning -- Transforms -- Order Statistics -- The Multivariate Normal Distribution -- Convergence -- An Outlook on Further Topics -- The Poisson Process. aThe purpose of this book is to provide the reader with a solid background and understanding of the basic results and methods in probability theory before entering into more advanced courses. The first six chapters focus on some central areas of what might be called pure probability theory: multivariate random variables, conditioning, transforms, order variables, the multivariate normal distribution, and convergence. A final chapter is devoted to the Poisson process as a means both to introduce stochastic processes and to apply many of the techniques introduced earlier in the text. Students are assumed to have taken a first course in probability, though no knowledge of measure theory is assumed. Throughout, the presentation is thorough and includes many examples that are discussed in detail. Thus, students considering more advanced research in probability theory will benefit from this wide-ranging survey of the subject that provides them with a foretaste of the subject's many treasures. The present second edition offers updated content, one hundred additional problems for solution, and a new chapter that provides an outlook on further areas and topics, such as stable distributions and domains of attraction, extreme value theory and records, and martingales. The main idea is that this chapter may serve as an appetizer to the more advanced theory. Allan Gut is Professor of Mathematical Statistics at Uppsala University, Uppsala, Sweden. He is a member of the International Statistical Institute, the Bernoulli Society, the Institute of Mathematical Statistics, and the Swedish Statistical Society. He is an Associate Editor of the Journal of Statistical Planning and Inference and Sequential Analysis, a former Associate Editor of the Scandinavian Journal of Statistics, and the author of five other books including Probability: A Graduate Course (Springer, 2005) and Stopped Random Walks: Limit Theorems and Applications, Second Edition (Springer, 2009). 0aMathematics. 0aComputers. 0aMathematical statistics. 0aLife sciences. 0aProbabilities. 0aStatistics. 0aEnvironmental sciences.14aMathematics.24aProbability Theory and Stochastic Processes.24aLife Sciences, general.24aTheory of Computation.24aStatistical Theory and Methods.24aProbability and Statistics in Computer Science.24aMath. Appl. in Environmental Science.2 aSpringerLink (Online service)0 tSpringer eBooks08iPrinted edition:z9781441901613 0aSpringer Texts in Statistics,x1431-875X40uhttp://dx.doi.org/10.1007/978-1-4419-0162-0