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Introduction to Bayesian Statistics.
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
New York :
Wiley,
2012.
|
| Edición: | 3rd ed. |
| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- INTRODUCTION TO BAYESIAN STATISTICS; Contents; Preface; Changes in the Third Edition; Our Perspective on Bayesian Statistics; Acknowledgments; 1 Introduction to Statistical Science; 1.1 The Scientific Method: A Process for Learning; 1.2 The Role of Statistics in the Scientific Method; 1.3 Main Approaches to Statistics; Frequentist Approach to Statistics; Bayesian Approach to Statistics; Monte Carlo Studies; 1.4 Purpose and Organization of This Text; 2 Scientific Data Gathering; 2.1 Sampling from a Real Population; Simple Random Sampling (without Replacement); Stratified Random Sampling.
- Non-sampling Errors in Sample SurveysRandomized Response Methods; 2.2 Observational Studies and Designed Experiments; Observational Study; Designed Experiment; Monte Carlo Exercises; 3 Displaying and Summarizing Data; 3.1 Graphically Displaying a Single Variable; Dotplot; Boxplot (Box-and-Whisker Plot); Stem-and-Leaf Diagram; Frequency Table; Histogram; Cumulative Frequency Polygon; 3.2 Graphically Comparing Two Samples; 3.3 Measures of Location; Mean: Advantages and Disadvantages; Median: Advantages and Disadvantages; 3.4 Measures of Spread; Range: Advantage and Disadvantage.
- Interquartile Range: Advantages and DisadvantagesVariance: Advantages and Disadvantages; Standard Deviation: Advantages and Disadvantages; 3.5 Displaying Relationships Between Two or More Variables; Scatterplot; Scatterplot Matrix; 3.6 Measures of Association for Two or More Variables; Covariance and Correlation between Two Variables; Exercises; 4 Logic, Probability, and Uncertainty; 4.1 Deductive Logic and Plausible Reasoning; Desired Properties of Plausibility Measures; 4.2 Probability; 4.3 Axioms of Probability; 4.4 Joint Probability and Independent Events; 4.5 Conditional Probability.
- 4.6 Bayes' TheoremBayes' Theorem: The Key to Bayesian Statistics; 4.7 Assigning Probabilities; 4.8 Odds and Bayes Factor; Bayes Factor (B); 4.9 Beat the Dealer; Exercises; 5 Discrete Random Variables; 5.1 Discrete Random Variables; 5.2 Probability Distribution of a Discrete Random Variable; Expected Value of a Discrete Random Variable; The Variance of a Discrete Random Variable; The Mean and Variance of a Linear Function of a Random Variable; 5.3 Binomial Distribution; Characteristics of the Binomial Distribution; 5.4 Hypergeometric Distribution; Probability Function of Hypergeometric.
- 5.5 Poisson DistributionCharacteristics of the Poisson Distribution; 5.6 Joint Random Variables; Independent Random Variables; 5.7 Conditional Probability for Joint Random Variables; Exercises; 6 Bayesian Inference for Discrete Random Variables; 6.1 Two Equivalent Ways of Using Bayes' Theorem; 6.2 Bayes' Theorem for Binomial with Discrete Prior; Setting up the Table for Bayes' Theorem on Binomial with Discrete Prior; 6.3 Important Consequences of Bayes' Theorem; 6.4 Bayes' Theorem for Poisson with Discrete Prior; Setting up the Table for Bayes' Theorem on Poisson with Discrete Prior.