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100 Go Mistakes and How to Avoid Them
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100 Go Mistakes and How to Avoid Them

by Teiva Harsanyi

Programming

Book Details

Book Title

100 Go Mistakes and How to Avoid Them

Author

Teiva Harsanyi

Publisher

Manning Publications

Publication Date

2022

ISBN

9781617299599

Number of Pages

385

Language

English

Format

PDF

File Size

3.9MB

Subject

Programming

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