Bayesian Statistics the Fun Way: Understanding Statistics and Probability with Star Wars, LEGO, and Rubber Ducks
- 3h 38m
- Will Kurt
- No Starch Press
- 2019
Fun guide to learning Bayesian statistics and probability through unusual and illustrative examples.
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 shower, 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.
About the Author
Will Kurt currently works as a Senior Data Scientist at Bombora, and has been using Bayesian statistics to solve real business problems for over half a decade. He frequently blogs about probability on his website, CountBayesie.com. Will is the author of Get Programming with Haskell (Manning Publications) and lives in Reno, Nevada.
In this Book
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Introduction
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Bayesian Thinking and Everyday Reasoning
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Measuring Uncertainty
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The Logic of Uncertainty
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Creating a Binomial Probability Distribution
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The Beta Distribution
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Conditional Probability
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Bayes’ Theorem with LEGO
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The Prior, Likelihood, and Posterior of Bayes' Theorem
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Bayesian Priors and Working with Probability Distributions
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Introduction to Averaging and Parameter Estimation
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Measuring the Spread of Our Data
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The Normal Distribution
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Tools of Parameter Estimation—The PDF, CDF, and Quantile Function
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Parameter Estimation with Prior Probabilities
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From Parameter Estimation to Hypothesis Testing—Building a Bayesian A/B Test
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Introduction to the Bayes Factor and Posterior Odds—The Competition of Ideas
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Bayesian Reasoning in the Twilight Zone
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When Data Doesn't Convince You
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From Hypothesis Testing to Parameter Estimation
