Bayesian Statistics the Fun Way

Bayesian Statistics the Fun Way

Understanding Statistics and Probability with Star Wars, LEGO, and Rubber Ducks
by Will Kurt
July 2019, 256 pp.

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Bayesian Statistics The Fun WayBayesian Statistics The Fun WayBayesian Statistics The Fun WayBayesian Statistics The Fun WayBayesian Statistics The Fun Way

Download Chapter 7: Bayes' Theorem with LEGO

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Get the most from your data, and have fun doing it

Probability and statistics are increasingly important in a huge range of professions. But many people use data in ways they don’t even understand, meaning they aren’t getting the most from it. Bayesian Statistics the Fun Way will change that.

This book will give you a complete understanding of Bayesian statistics through simple explanations and un-boring examples. Find out the probability of UFOs landing in your garden, how likely Han Solo is to survive a flight through an asteroid belt, how to win an argument about conspiracy theories, and whether a burglary really was a burglary, to name a few examples.

By using these off-the-beaten-track examples, the author actually makes learning statistics fun. And you’ll learn real skills, like how to:

  • How to measure your own level of uncertainty in a conclusion or belief
  • Calculate Bayes theorem and understand what it’s useful for
  • Find the posterior, likelihood, and prior to check the accuracy of your conclusions
  • Calculate distributions to see the range of your data
  • Compare hypotheses and draw reliable conclusions from them

Next time you find yourself with a sheaf of survey results and no idea what to do with them, turn to Bayesian Statistics the Fun Way to get the most value from your data.

Author Bio 

Will Kurt works as a data scientist at Wayfair, and has been using Bayesian statistics to solve real business problems for over half a decade. He frequently blogs about probability on his website, Kurt is the author of Get Programming with Haskell (Manning Publications) and lives in Boston, Massachusetts.

Table of contents 


Part 1: Introduction to Probability
Chapter 1: What Do You Believe and How Do You Change it?
Chapter 2: Measuring Uncertainty
Chapter 3: The Logic of Uncertainty
Chapter 4: Probability Distributions 1
Chapter 5: Probability Distributions 2

Part 2: Bayesian Probability and Prior Probabilities
Chapter 6: Conditional Probability
Chapter 7: Bayes' Theorem with LEGO
Chapter 8: Posterior, Likelihood, and Prior
Chapter 9: Working with Prior Probability Distributions

Part 3: Parameter Estimation
Chapter 10: Intro to Parameter Estimation
Chapter 11: Measuring the Spread of Data
Chapter 12: Normal Distribution and Confidence
Chapter 13: Tools of Parameter Estimation
Chapter 14: Parameter Estimation with Priors

Part 4: Hypothesis Testing: The Heart of Statistics
Chapter 15: From Parameter Estimation to Hypothesis Testing
Chapter 16: Comparing Hypotheses with Bayes Factor
Chapter 17: Bayesian Reasoning in the Twilight Zone
Chapter 18: When Data Doesn't Convince You
Chapter 19: From Hypothesis Testing to Parameter Estimation

Appendix A: A Crash Course in R
Appendix B: Enough Calculus to Get By

View the detailed Table of Contents
View the Index


"An excellent introduction to subjects critical to all data scientists." —Inside Big Data

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