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Bayesian statistics the fun way : understanding statistics and probability with Star Wars, LEGO, and rubber ducks /
"An introduction to Bayesian statistics with simple and pop culture-based explanations. Topics covered include measuring your own uncertainty in a belief, applying Bayes' theorem, and calculating distributions"--
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
San Francisco :
No Starch Press, Inc.,
[2019]
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| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
Tabla de Contenidos:
- Part 1. Introduction to probability
- Ch. 1. Bayesian thinking and everyday reasoning
- Ch. 2. Measuring uncertainty
- Ch. 3. The logic of uncertainty
- Ch. 4. Creating a binomial probability distribution
- Ch. 5. The beta distribution
- Part 2. Bayesian probability and prior probabilities
- Ch. 6. Conditional probability
- Ch. 7. Bayes' theorem with LEGO
- Ch. 8. The prior, likelihood, and posterior of Bayes' theorem
- Ch. 9. Bayesian priors and working with probability distributions
- Part 3. Parameter estimation
- Ch. 10. Introduction to averaging and parameter estimation
- Ch. 11. Measuring the spread of our data
- Ch. 12. The normal distribution
- Ch. 13. Tools of parameter estimation : the PDF, CDF, and Quantile function
- Ch. 14. Parameter estimation with prior probabilities
- Part 4. Hypothesis testing : the heart of statistics
- Ch. 15. From parameter estimation to hypothesis testing : building a Bayesian A/B test
- Ch. 16. Introduction to the Bayes factor and posterior odds : the competition of ideas
- Ch. 17. Bayesian reasoning in the twilight zone
- Ch. 18. When data doesn't convince you
- Ch. 19. From hypothesis testing to parameter estimation
- Appendix A: A quick introduction to R
- Appendix B: Enough calculus to get by.
- Intro; Brief Contents; Contents in Detail; Acknowledgments; Introduction; Why Learn Statistics?; What Is "Bayesian" Statistics?; What's in This Book; Part I: Introduction to Probability; Part II: Bayesian Probability and Prior Probabilities; Part III: Parameter Estimation; Part IV: Hypothesis Testing: The Heart of Statistics; Background for Reading the Book; Now Off on Your Adventure!; Part I: Introduction to Probability; Chapter 1: Bayesian Thinking and Everyday Reasoning; Reasoning About Strange Experiences; Observing Data; Holding Prior Beliefs and Conditioning Probabilities
- Forming a HypothesisSpotting Hypotheses in Everyday Speech; Gathering More Evidence and Updating Your Beliefs; Comparing Hypotheses; Data Informs Belief; Belief Should Not Inform Data; Wrapping Up; Exercises; Chapter 2: Measuring Uncertainty; What Is a Probability?; Calculating Probabilities by Counting Outcomes of Events; Calculating Probabilities as Ratios of Beliefs; Using Odds to Determine Probability; Solving for the Probabilities; Measuring Beliefs in a Coin Toss; Wrapping Up; Exercises; Chapter 3: The Logic of Uncertainty; Combining Probabilities with AND
- Solving a Combination of Two ProbabilitiesApplying the Product Rule of Probability; Example: Calculating the Probability of Being Late; Combining Probabilities with OR; Calculating OR for Mutually Exclusive Events; Using the Sum Rule for Non-Mutually Exclusive Events; Example: Calculating the Probability of Getting a Hefty Fine; Wrapping Up; Exercises; Chapter 4: Creating a Binomial Probability Distribution; Structure of a Binomial Distribution; Understanding and Abstracting Out the Details of Our Problem; Counting Our Outcomes with the Binomial Coefficient
- Combinatorics: Advanced Counting with the Binomial CoefficientCalculating the Probability of the Desired Outcome; Example: Gacha Games; Wrapping Up; Exercises; Chapter 5: The Beta Distribution; A Strange Scenario: Getting the Data; Distinguishing Probability, Statistics, and Inference; Collecting Data; Calculating the Probability of Probabilities; The Beta Distribution; Breaking Down the Probability Density Function; Applying the Probability Density Function to Our Problem; Quantifying Continuous Distributions with Integration; Reverse-Engineering the Gacha Game; Wrapping Up; Exercises
- Part II: Bayesian Probability and Prior ProbabilitiesChapter 6: Conditional Probability; Introducing Conditional Probability; Why Conditional Probabilities Are Important; Dependence and the Revised Rules of Probability; Conditional Probabilities in Reverse and Bayes' Theorem; Introducing Bayes' Theorem; Wrapping Up; Exercises; Chapter 7: Bayes' Theorem with LEGO; Working Out Conditional Probabilities Visually; Working Through the Math; Wrapping Up; Exercises; Chapter 8: The Prior, Likelihood, and Posterior of Bayes' Theorem; The Three Parts; Investigating the Scene of a Crime