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Think Bayes (2nd Edition)
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Think Bayes (2nd Edition)

by Allen Downey

Data Science

Book Details

Book Title

Think Bayes, 2nd Edition Edition: 2

Author

Allen Downey

Publisher

.O'Reilly Media, Inc

Publication Date

2021

ISBN

9781492089469

Number of Pages

440

Language

English

Format

PDF

File Size

4.26MB

Subject

Statistics

Table of Contents

  • Preface
  • Chapter 1: Probability
  • Linda the Banker
  • Probability
  • Fraction of Bankers
  • The Probability Function
  • Political Views and Parties
  • Conjunction
  • Conditional Probability
  • Conditional Probability Is Not Commutative
  • Condition and Conjunction
  • Laws of Probability
  • Summary
  • Exercises
  • Chapter 2: Bayes’s Theorem
  • The Cookie Problem
  • Diachronic Bayes
  • Bayes Tables
  • The Dice Problem
  • The Monty Hall Problem
  • Summary
  • Exercises
  • Chapter 3: Distributions
  • Distributions
  • Probability Mass Functions
  • The Cookie Problem Revisited
  • 101 Bowls
  • The Dice Problem
  • Updating Dice
  • Summary
  • Exercises
  • Chapter 4: Estimating Proportions
  • The Euro Problem
  • The Binomial Distribution
  • Bayesian Estimation
  • Triangle Prior
  • The Binomial Likelihood Function
  • Bayesian Statistics
  • Summary
  • Exercises
  • Chapter 5: Estimating Counts
  • The Train Problem
  • Sensitivity to the Prior
  • Power Law Prior
  • Credible Intervals
  • The German Tank Problem
  • Informative Priors
  • Summary
  • Exercises
  • Chapter 6: Odds and Addends
  • Odds
  • Bayes’s Rule
  • Oliver’s Blood
  • Addends
  • Gluten Sensitivity
  • The Forward Problem
  • The Inverse Problem
  • Summary
  • More Exercises
  • Chapter 7: Minimum, Maximum, and Mixture
  • Cumulative Distribution Functions
  • Best Three of Four
  • Maximum
  • Minimum
  • Mixture
  • General Mixtures
  • Summary
  • Exercises
  • Chapter 8: Poisson Processes
  • The World Cup Problem
  • The Poisson Distribution
  • The Gamma Distribution
  • The Update
  • Probability of Superiority
  • Predicting the Rematch
  • The Exponential Distribution
  • Summary
  • Exercises
  • Chapter 9: Decision Analysis
  • The Price Is Right Problem
  • The Prior
  • Kernel Density Estimation
  • Distribution of Error
  • Update
  • Probability of Winning
  • Decision Analysis
  • Maximizing Expected Gain
  • Summary
  • Discussion
  • More Exercises
  • Chapter 10: Testing
  • Estimation
  • Evidence
  • Uniformly Distributed Bias
  • Bayesian Hypothesis Testing
  • Bayesian Bandits
  • Prior Beliefs
  • The Update
  • Multiple Bandits
  • Explore and Exploit
  • The Strategy
  • Summary
  • More Exercises
  • Chapter 11: Comparison
  • Outer Operations
  • How Tall Is A?
  • Joint Distribution
  • Visualizing the Joint Distribution
  • Likelihood
  • The Update
  • Marginal Distributions
  • Conditional Posteriors
  • Dependence and Independence
  • Summary
  • Exercises
  • Chapter 12: Classification
  • Penguin Data
  • Normal Models
  • The Update
  • Naive Bayesian Classification
  • Joint Distributions
  • Multivariate Normal Distribution
  • A Less Naive Classifier
  • Summary
  • Exercises
  • Chapter 13: Inference
  • Improving Reading Ability
  • Estimating Parameters
  • Likelihood
  • Posterior Marginal Distributions
  • Distribution of Differences
  • Using Summary Statistics
  • Update with Summary Statistics
  • Comparing Marginals
  • Summary
  • Exercises
  • Chapter 14: Survival Analysis
  • The Weibull Distribution
  • Incomplete Data
  • Using Incomplete Data
  • Light Bulbs
  • Posterior Means
  • Posterior Predictive Distribution
  • Summary
  • Exercises
  • Chapter 15: Mark and Recapture
  • The Grizzly Bear Problem
  • The Update
  • Two-Parameter Model
  • The Prior
  • The Update
  • The Lincoln Index Problem
  • Three-Parameter Model
  • Summary
  • Exercises
  • Chapter 16: Logistic Regression
  • Log Odds
  • The Space Shuttle Problem
  • Prior Distribution
  • Likelihood
  • The Update
  • Marginal Distributions
  • Transforming Distributions
  • Predictive Distributions
  • Empirical Bayes
  • Summary
  • More Exercises
  • Chapter 17: Regression
  • More Snow?
  • Regression Model
  • Least Squares Regression
  • Priors
  • Likelihood
  • The Update
  • Marathon World Record
  • The Priors
  • Prediction
  • Summary
  • Exercises
  • Chapter 18: Conjugate Priors
  • The World Cup Problem Revisited
  • The Conjugate Prior
  • What the Actual?
  • Binomial Likelihood
  • Lions and Tigers and Bears
  • The Dirichlet Distribution
  • Summary
  • Exercises
  • Chapter 19: MCMC
  • The World Cup Problem
  • Grid Approximation
  • Prior Predictive Distribution
  • Introducing PyMC3
  • Sampling the Prior
  • When Do We Get to Inference?
  • Posterior Predictive Distribution
  • Happiness
  • Simple Regression
  • Multiple Regression
  • Summary
  • Exercises
  • Chapter 20: Approximate Bayesian Computation
  • The Kidney Tumor Problem
  • A Simple Growth Model
  • A More General Model
  • Simulation
  • Approximate Bayesian Computation
  • Counting Cells
  • Cell Counting with ABC
  • When Do We Get to the Approximate Part?
  • Summary
  • Exercises
  • Index