LEADER 05309nam 2200649Ia 450 001 9910144709403321 005 20170815114228.0 010 $a1-281-20382-3 010 $a9786611203825 010 $a0-470-19159-7 010 $a0-470-19158-9 035 $a(CKB)1000000000377287 035 $a(EBL)331566 035 $a(OCoLC)476131012 035 $a(SSID)ssj0000227863 035 $a(PQKBManifestationID)11173591 035 $a(PQKBTitleCode)TC0000227863 035 $a(PQKBWorkID)10289999 035 $a(PQKB)10069613 035 $a(MiAaPQ)EBC331566 035 $a(EXLCZ)991000000000377287 100 $a20070815d2008 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aProbability and statistical inference$b[electronic resource] /$fRobert Bartoszyn?ski and Magdalena Niewiadomska-Bugaj 205 $a2nd ed. 210 $aHoboken, N.J. ;$a[Chichester] $cWiley-Interscience$dc2008 215 $a1 online resource (662 p.) 300 $aPrevious ed.: Chichester: Wiley, 1996. 311 $a0-471-69693-5 320 $aIncludes bibliographical references and index. 327 $aPROBABILITY AND STATISTICAL INFERENCE; CONTENTS; Preface; 1 Experiments, Sample Spaces, and Events; 1.1 Introduction; 1.2 Sample Space; 1.3 Algebra of Events; 1.4 Infinite Operations on Events; 2 Probability; 2.1 Introduction; 2.2 Probability as a Frequency; 2.3 Axioms of Probability; 2.4 Consequences of the Axioms; 2.5 Classical Probability; 2.6 Necessity of the Axioms; 2.7 Subjective Probability; 3 Counting; 3.1 Introduction; 3.2 Product Sets, Orderings, and Permutations; 3.3 Binomial Coefficients; 3.4 Extension of Newton's Formula; 3.5 Multinomial Coefficients; 4 Conditional Probability 327 $aIndependence4.1 Introduction; 4.2 Conditional Probability; 4.3 Partitions; Total Probability Formula; 4.4 Bayes' Formula; 4.5 Independence; 4.6 Exchangeability; Conditional Independence; 5 Markov Chains; 5.1 Introduction and Basic Definitions; 5.2 Definition of a Markov Chain; 5.3 n-Step Transition Probabilities; 5.4 The Ergodic Theorem; 5.5 Absorption Probabilities; 6 Random Variables: Univariate Case; 6.1 Introduction; 6.2 Distributions of Random Variables; 6.3 Discrete and Continuous Random Variables; 6.4 Functions of Random Variables; 6.5 Survival and Hazard Functions 327 $a7 Random Variables: Multivariate Case7.1 Bivariate Distributions; 7.2 Marginal Distributions; Independence; 7.3 Conditional Distributions; 7.4 Bivariate Transformations; 7.5 Multidimensional Distributions; 8 Expectation; 8.1 Introduction; 8.2 Expected Value; 8.3 Expectation as an Integral; 8.4 Properties of Expectation; 8.5 Moments; 8.6 Variance; 8.7 Conditional Expectation; 8.8 Inequalities; 9 Selected Families of Distributions; 9.1 Bernoulli Trials and Related Distributions; 9.2 Hypergeometric Distribution; 9.3 Poisson Distribution and Poisson Process 327 $a9.4 Exponential, Gamma and Related Distributions9.5 Normal Distribution; 9.6 Beta Distribution; 10 Random Samples; 10.1 Statistics and their Distributions; 10.2 Distributions Related to Normal; 10.3 Order Statistics; 10.4 Generating Random Samples; 10.5 Convergence; 11.5 Sampling; 10.6 Central Limit Theorem; 11 Introduction to Statistical Inference; 11.1 Overview; 11.2 Descriptive Statistics; 11.3 Basic Model; 11.4 Bayesian Statistics; 11.6 Measurement Scales; 12 Estimation; 12.1 Introduction; 12.2 Consistency; 12.3 Loss, Risk, and Admissibility; 12.4 Efficiency 327 $a12.5 Methods of Obtaining Estimators12.6 Sufficiency; 12.7 Interval Estimation; 13 Testing Statistical Hypotheses; 13.1 Introduction; 13.2 Intuitive Background; 13.3 Most Powerful Tests; 13.4 Uniformly Most Powerful Tests; 13.5 Unbiased Tests; 13.6 Generalized Likelihood Ratio Tests; 13.7 Conditional Tests; 13.8 Tests and Confidence Intervals; 13.9 Review of Tests for Normal Distributions; 13.10 Monte Carlo, Bootstrap, and Permutation Tests; 14 Linear Models; 14.1 Introduction; 14.2 Regression of the First and Second Kind; 14.3 Distributional Assumptions 327 $a14.4 Linear Regression in the Normal Case 330 $aNow updated in a valuable new edition-this user-friendly book focuses on understanding the ""why"" of mathematical statistics Probability and Statistical Inference, Second Edition introduces key probability and statis-tical concepts through non-trivial, real-world examples and promotes the developmentof intuition rather than simple application. With its coverage of the recent advancements in computer-intensive methods, this update successfully provides the comp-rehensive tools needed to develop a broad understanding of the theory of statisticsand its probabilistic foundations. This outstandi 606 $aProbabilities 606 $aMathematical statistics 608 $aElectronic books. 615 0$aProbabilities. 615 0$aMathematical statistics. 676 $a519 676 $a519.54 700 $aBartoszyn?ski$b Robert$0984009 701 $aNiewiadomska-Bugaj$b Magdalena$0503998 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910144709403321 996 $aProbability and statistical inference$92246825 997 $aUNINA