Written by three veteran statisticians, this applied introduction to probability and statistics emphasizes the existence of variation in almost every process, and how the study of probability and statistics helps us understand this variation. Designed for students with a background in calculus, this book continues to reinforce basic mathematical concepts with numerous real-world examples and applications to illustrate the relevance of key concepts.

针对实际监控中人体目标轮廓的多尺度特性，提出一种用于人体目标检测的多尺度方向特征描述子(HOGG)。首先采用Gabor滤波器提取人体图像对应不同尺度、不同方向的多个Gabor幅值域图谱，然后将相同尺度不同方向的幅值域图谱融合以降低特征维数，并对每幅融合图像提取梯度方向直方图(HOG)特征，最后将这些HOG特征联合起来作为人体图像表征。利用支持向量机(SVM)对描述特征进行分类，在CAVIAR数据库中进行了实验，结果表明，该算法对人体目标检测具有较好的性能。

Introduction to Mathematical Statistics_6th edition_Hogg McKean Craig.pdf 第六版

第七版 Indroduction_to_Mathematica_Statistics.Hogg,McKean,Craig seventh edition.pdf UIUC的 STAT401统计课程教材 Contents Preface ix 1 Probability and Distributions 1 1.1 Introduction................................ 1 1.2 SetTheory ................................ 3 1.3 TheProbabilitySetFunction ...................... 10 1.4 ConditionalProbabilityandIndependence . . . . . . . . . . . . . . . 21 1.5 RandomVariables ............................ 32 1.6 DiscreteRandomVariables ....................... 40 1.6.1 Transformations ......................... 42 1.7 ContinuousRandomVariables...................... 44 1.7.1 Transformations ......................... 46 1.8 ExpectationofaRandomVariable ................... 52 1.9 SomeSpecialExpectations ....................... 57 1.10ImportantInequalities .......................... 68 2 Multivariate Distributions 73 2.1 DistributionsofTwoRandomVariables ................ 73 2.1.1 Expectation............................ 79 2.2 Transformations: Bivariate Random Variables . . . . . . . . . . . . . 84 2.3 Conditional Distributions and Expectations . . . . . . . . . . . . . . 94 2.4 TheCorrelationCoefficient ....................... 102 2.5 IndependentRandomVariables.....................110 2.6 ExtensiontoSeveralRandomVariables . . . . . . . . . . . . . . . . 117 2.6.1 ∗Multivariate Variance-Covariance Matrix . . . . . . . . . . . 123 2.7 Transformations for Several Random Variables . . . . . . . . . . . . 126 2.8 LinearCombinationsofRandomVariables. . . . . . . . . . . . . . . 134 3 Some Special Distributions 139 3.1 TheBinomialandRelatedDistributions . . . . . . . . . . . . . . . . 139 3.2 ThePoissonDistribution ........................ 150 3.3 TheΓ,χ2,andβDistributions ..................... 156 3.4 TheNormalDistribution......................... 168 3.4.1 ContaminatedNormals ..................... 174 v vi Contents 3.5 TheMultivariateNormalDistribution ................. 178 3.5.1 ∗Applications........................... 185 3.6 t-andF-Distributions

Solution.Manual.to.Introduction.to.Mathematical.statistics. Hogg..McKean.and.Craig