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  • 醉染图书概率统计(英文第4版)9787111387756
  • 正版全新
    • 作者: (美)德格鲁特 等著 | (美)德格鲁特 等编 | (美)德格鲁特 等译 | (美)德格鲁特 等绘
    • 出版社: 机械工业出版社
    • 出版时间:2012-07-01
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    • 作者: (美)德格鲁特 等著| (美)德格鲁特 等编| (美)德格鲁特 等译| (美)德格鲁特 等绘
    • 出版社:机械工业出版社
    • 出版时间:2012-07-01
    • 版次:1
    • 印次:1
    • 页数:891
    • ISBN:9787111387756
    • 国别/地区:中国
    • 版权提供:机械工业出版社
    • 作者:(美)德格鲁特 等
    • 著:(美)德格鲁特 等
    • 装帧:平装
    • 印次:1
    • 定价:139.00
    • ISBN:9787111387756
    • 出版社:机械工业出版社
    • 开本:暂无
    • 印刷时间:暂无
    • 语种:暂无
    • 出版时间:2012-07-01
    • 页数:891
    • 外部编号:1200300713
    • 版次:1
    • 成品尺寸:暂无

    1 introduction to probability 1
    1.1 the history of probability 1
    1.2 interpretations of probability 2
    1.3 experiments and events 5
    1.4 set theory 6
    1.5 the definition of probability 16
    1.6 finite sample spaces 22
    1.7 counting methods 25
    1.8 combinatorial methods 32
    1.9 multinomial coefficients 42
    1.10 the probability of a union of events 46
    1.11 statistical swindles 51
    1.12 supplementary exercises 53

    2 conditional probability 55
    2.1 the definition of conditional probability 55
    2.2 independent events 66
    . bayes’ theorem 76
    2.4 the gambler’s ruin problem 86
    2.5 supplementary exercises 90

    3 random variables and distributions 93
    3.1 random variables and discrete distributions 93
    3.2 continuous distributions 100
    3.3 the cumulative distribution function 107
    3.4 bivariate distributions 118
    3.5 marginal distributions 130
    3.6 conditional distributions 141
    3.7 multivariate distributions 152
    3.8 functions of a random variable 167
    3.9 functions of two or more random variables 175
    3.10 markov chains 188
    3.11 supplementary exercises 202

    4 expectation 207
    4.1 the expectation of a random variable 207
    4.2 properties of expectations 217
    4.3 variance 225
    4.4 moments 4
    4.5 the mean and the median 241
    4.6 covariance and correlation 248
    4.7 conditional expectation 256
    4.8 utility 265
    4.9 supplementary exercises 272

    5 spe distributions 275
    5.1 introduction 275
    5.2 the bernoulli and binomial distributions 275
    5.3 the hypergeometric distributions 281
    5.4 the poisson distributions 287
    5.5 the negative binomial distributions 297
    5.6 the normal distributions 302
    5.7 the gamma distributions 316
    5.8 the beta distributions 327
    5.9 the multinomial distributions 333
    5.10 the bivariate normal distributions 337
    5.11 supplementary exercises 345

    6 large random samples 347
    6.1 introduction 347
    6.2 the law o are numbers 348
    6.3 the central limit theorem 360
    6.4 the correction for continuity 371
    6.5 supplementary exercises 375

    7 estimation 376
    7.1 statistical inference 376
    7.2 prior and posterior distributions 385
    7.3 conjugate prior distributions 394
    7.4 bayes estimators 408
    7.5 maximum likelihood estimators 417
    7.6 properties of maximum likelihood estimators 426
    7.7 sufficient statistics 443
    7.8 jointly sufficient statistics 449
    7.9 improving an estimator 455
    7.10 supplementary exercises 461

    8 sampling distributions of estimators 464
    8.1 the sampling distribution of a statistic 464
    8.2 the chi-square distributions 469
    8.3 joint distribution of the sample mean and sample variance 473
    8.4 the t distributions 480
    8.5 confidence intervals 485
    8.6 bayesian analysis of samples from a normal distribution 495
    8.7 unbiased estimators 506
    8.8 fisher information 514
    8.9 supplementary exercises 528

    9 testing hypotheses 530
    9.1 problems of testing hypotheses 530
    9.2 testing simple hypotheses 550
    9.3 uniformly most powerful tests 559
    9.4 two-sided alternatives 567
    9.5 the t test 576
    9.6 comparing the means of two normal distributions 587
    9.7 the f distributions 597
    9.8 bayes test procedures 605
    9.9 foundational issues 617
    9.10 supplementary exercises 621

    10 categorical data and nonparametric methods 624
    10.1 tests of goodness-of-fit 624
    10.2 goodness-of-fit for coite hypotheses 633
    10.3 contingency tables 641
    10.4 tests of homogeneity 647
    10.5 sisn’ paradox 653
    10.6 kooorov-smirnov tests 657
    10.7 robust estimation 666
    10.8 sign and rank tests 678
    10.9 supplementary exercises 686

    11 linear statistical models 689
    11.1 te eho of least squares 689
    11.2 regression 698
    11.3 statistical inference in simple linear regression 707
    11.4 bayesian inference in simple linear regression 729
    11.5 the general linear model an mtple regression 736
    11.6 analysis of variance 754
    11.7 the two-way layout 763
    11.8 the two-way layout with replications 772
    11.9 supplementary exercises 783

    12 simulation 787
    12.1 what is simulation? 787
    12.2 why is simulation useful? 791
    1. simulating specific distributions 804
    12.4 importance sampling 816
    12.5 markov chain monte carlo 8
    12.6 the bootstrap 839
    12.7 supplementary exercises 850

    tables 853
    answers to odd-numbered exercises 865
    references 879
    index 885

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