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Name Index.- Abbreviation and Notation References.- Distributions.- Multivariate D.3 Distributions.- Discrete Univariate D.2 Distributions.- Continuous Univariate D.1 Distributions.- of Summary D: Appendix Analysis.- Functional C.3 Analysis.- Complex C.2 Analysis.- Real C.1 Here.- Proven Not Theorems Mathematical C: Appendix Problems.- B.8 Simulation*.- B.7 Probability.- Subjective B.6 Processes.- Stochastic General B.5.4 Chains*.- Markov B.5.3 Introduction.- B.5.1 Processes.- Stochastic B.5 Functions.- Characteristic B.4.2 Probability.- in and Distribution in Convergence B.4.1 Theorems.- Limit B.4 Probability.- Total of Law The B.3.5 Independence.- Conditional B.3.4 Densities.- Conditional B.3.3 Spaces*.- Borel B.3.2 Expectations.- Conditional B.3.1 Conditioning.- B.3 Inequalities.- Useful Some B.2.2 Distributions.- and Quantities Random B.2.1 Probability.- Mathematical B.2 Theorems.- Limit B.1.3 Conditioning.- B.1.2 Probability.- Mathematical B.1.1 Overview.- B.1 Theory.- Probability B: Appendix Problems.- A.7 Continuity.- Absolute A.6 Spaces.- Product A.5 Integration.- A.4 Functions.- Measurable A.3 Measures.- A.2 Continuity.- Absolute A.1.4 Integration.- A.1.3 Functions.- Measurable A.1.2 Definitions.- A.1.1 Overview.- A.1 Theory.- Integration and Measure A: Appendix Problems.- 9.5 Rules.- Stopping of Relevancc The 9.4 Estimation*.- Interval 9.3 Test.- Ratio Probability Sequential The 9.2 Problems.- Decision Sequential 9.1 Analysis.- Sequential 9: Problems.- 8.7 Robustness.- Bayesian 8.6.3 Outliers.- 8.6.2 Models.- Mixture General 8.6.1 Models.- of Mixtures 8.6 Models.- Nonnormal 8.5.3 Models.- Hierarchical Normal 8.5.2 Algorithm.- General The 8.5.1 Sampling.- Substitution Successive 8.5 Case.- Variance Unequal 8.4.3 Bayes.- Empirical Adjusted 8.4.2 Bayes.- Empirical Naïve 8.4.1 Analysis*.- Bayes Empirical 8.4 Data.- Process Bernoulli 8.3.2 Data.- Process Poisson 8.3.1 Models*.- Nonnormal 8.3 Testing.- Hypothesis 8.2.3 ANOVA*.- Model Mixed Two-Way 8.2.2 ANOVA.- One-Way 8.2.1 Models.- Linear Normal 8.2 Theorem*.- Representation the of Examples 8.1.3 Exchangeability*.- Partial 8.1.2 Models.- Hierarchical General 8.1.1 Introduction.- 8.1 Models.- Hierarchical 8: Problems.- 7.6 Tests.- Fit of Goodness Chi-Squarcd 7.5.2 Tests.- Ratio Likelihood 7.5.1 Tests.- Sample Large 7.5 Distributions+.- Predictive of Agreement Asymptotic 7.4.4 Distributions*.- Posterior to Approximations Laplace 7.4.3 Distributions.- Posterior of Normality Asymptotic 7.4.2 Distributions+.- Posterior of Consistency 7.4.1 Distributions.- Posterior of Properties Sample Large 7.4 M-Estimators.- of Properties Asymptotic 7.3.6 MLEs.- of Normality Asymptotic 7.3.5 MLEs.- Inconsistent of Examples 7.3.4 Families.- Exponential in MLEs 7.3.3 Estimators.- Likelihood Maximum 7.3.2 Estimation.- Sample Large of Principles Some 7.3.1 Estimation.- Sample Large 7.3 Quantiles*.- of Combinations Linear 7.2.3 Quantiles.- Several 7.2.2 Quantile.- Single A 7.2.1 Quantiles.- Sample 7.2 Method.- Delta The 7.1.3 Convergence.- Stochastic 7.1.2 Convergence.- Deterministic 7.1.1 Concepts.- Convergence 7.1 Theory.- Sample Large 7: Problems.- 6.4 Tests*.- Invariant 6.3.3 Sets.- Confidence Equivariant 6.3.2 Problems.- Invariant in P-Values 6.3.1 Intervals*.- Confidence and Testing 6.3 Decisions.- Equivariant Risk Minimum 6.2.3 Units.- of Changes and Equivariance 6.2.2 Transformations.- of Groups 6.2.1 Theory.- Decision Equivariant 6.2 Problems.- Scale 6.1.2 Problems.- Location 6.1.1 Examples.- Common 6.1 Equivariance*.- 6: Problems.- 5.4 Intervals.- Confidence Bootstrap 5.3.3 Bias.- and Deviations Standard 5.3.2 Concept.- General The 5.3.1 Bootstrap*.- The 5.3 Estimation.- Set Theoretic Decision 5.2.5 Estimation.- Set Bayesian 5.2.4 Sets*.- Tolerance 5.2.3 Sets*.- Prediction 5.2.2 Sets.- Confidence 5.2.1 Estimation.- Set 5.2 Estimation*.- Robust 5.1.5 Estimation.- Bayesian 5.1.4 Estimation.- Likelihood Maximum 5.1.3 Estimators.- Unbiased of Variance the on Bounds Lower 5.1.2 Estimation.- Unbiased Variance Minimum 5.1.1 Estimation.- Point 5.1 Estimation.- 5: Problems.- 4.7 Factors.- Bayes and P-Values 4.6.2 Examples.- and Definitions 4.6.1 P-Values.- 4.6 Rule.- Bayes a as F-Test Standard The 4.5.6 Tests.- Ratio Likelihood 4.5.5 Cases*.- Two-Sided Other 4.5.4 Parameters.- Natural of Combinations Linear 4.5.3 Parameters.- Natural about Tests 4.5.2 Structure.- Neyinan 4.5.1 Parameters.- Nuisance 4.5 Hypotheses.- Point 4.4.3 Hypotheses.- Interval 4.4.2 Results.- General 4.4.1 Tests.- Unbiased 4.4 Hypotheses.- Two-Sided 4.3.4 Tests.- One-Sided 4.3.3 Alternatives.- Composite Hypotheses, Simple 4.3.2 Alternatives.- and Hypotheses Simple 4.3.1 Tests.- Powerful Most 4.3 Factors.- Bayes 4.2.2 General.- in Testing 4.2.1 Solutions.- Bayesian 4.2 Tests.- Significance Pure 4.1.2 Problem.- Decision of Kind Special A 4.1.1 Introduction.- 4.1 Testing.- Hypothesis 4: Problems.- 3.4 Utility*.- State-Dependent 3.3.6 Theorems*.- Main the of Proofs 3.3.5 Theory.- Decision to Relation 3.3.4 Theorems.- Main The 3.3.3 Examples.- 3.2.2 Axioms.- and Definitions 3.3.1 Theory*.- Decision of Derivation Axiomatic 3.3 Classes.- Complete 3.2.5 Rules.- Minimax 3.2.4 Estimators.- James-Stein 3.2.3 Admissibility.- 3.2.2 Statistics.- Sufficient of Role The 3.2.1 Theory.- Decision Classical 3.2 Summary.- 3.1.4 Theory.- Decision Classical of Elements 3.1.3 Theory.- Decision Bayesian of Elements 3.1.2 Framework.- 3.1.1 Problems.- Decision 3.1 Theory.- Decision 3: Chapte Problems.- 2.5 Proofs+.- 2.4.3 Examples.- 2.4.2 Results.- Main The 2.4.1 Families*.- Extremal 2.4 Prior*.- Jeffreys' 2.3.4 Information*.- Conditional 2.3.3 Information.- Kullback-Leibler 2.3.2 Information.- Fisher 2.3.1 Information.- 2.3 Theorem*.- Characterization A 2.2.3 Properties.- Smoothness 2.2.2 Properties.- Basic 2.2.1 Distributions.- of Families Exponential 2.2 Ancillarity.- 2.1.4 Sufficiency.- Complete and Minimal 2.1.3 Sufficiency.- 2.1.2 Overview.- Notational 2.1.1 Definitions.- 2.1 Statistics.- Sufficient 2: Problems.- 1.7 Processes+.- Tailfree 1.6.2 Processes.- Dirichlet 1.6.1 Parameters*.- Infinite-Dimensional 1.6 Models*.- Parametric to Introduction Formal 1.5.5 Case.- Infinite General The 1.5.4 Case*.- Finite General The 1.5.3 Case.- Bernoulli The 1.5.2 Numbers.- Large of Law Strong 1.5.1 Results*.- Related and Theorem DeFinetti's of Proofs 1.5 Examples.- Some 1.4.3 Statements.- Mathematical The 1.4.2 Theorems.- the Understanding 1.4.1 Theorem.- Representation DeFinetti's 1.4 Distributions.- Probability Choosing 1.3.3 Distributions.- Prior Improper 1.3.2 Distributions.- Predictive and Posterior, Prior, 1.3.1 Models.- Parametric 1.3 Exchangeability.- arid Frequency 1.2.2 Symmetry.- Distributional 1.2.1 Exchangeability.- 1.2 Statistics.- Bayesian 1.1.3 Statistics.- Classical 1.1.2 Concepts.- General 1.1.1 Background.- 1.1 Models.- Probability 1: Content.- Darüber hinaus werden Verpackung und deren Kosten sowie der entsprechende Kundenservice Angebot Einkaufstasche 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