Bayesian statistics the fun way (eBook, 2019) []
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Bayesian statistics the fun way

Author: Will Kurt
Publisher: San Francisco : No Starch Press, Inc., [2019] ©2019
Edition/Format:   eBook : Document : EnglishView all editions and formats
"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"--

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Genre/Form: Electronic books
Additional Physical Format: Print version:
Kurt, Will, author.
Bayesian statistics the fun way
San Francisco : No Starch Press, Inc., [2019]
(DLC) 2019020743
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Will Kurt
ISBN: 1593279574 9781593279578
OCLC Number: 1100450254
Notes: Includes index.
Description: 1 online resource (xxii, 236 pages) : illustrations
Contents: Part 1. Introduction to probability. Bayesian thinking and everyday reasoning --
Measuring uncertainty --
The logic of uncertainty --
Creating a binomial probability distribution --
The beta distribution --
Part 2. Bayesian probability and prior probabilities. Conditional probability --
Bayes' theorem with LEGO --
The prior, likelihood, and posterior of Bayes' theorem --
Bayesian priors and working with probability distributions --
Part 3. Parameter estimation. Introduction to averaging and parameter estimation --
Measuring the spread of our data --
The normal distribution --
Tools of parameter estimation : the PDF, CDF, and Quantile function --
Parameter estimation with prior probabilities --
Part 4. Hypothesis testing: the heart of statistics. From parameter estimation to hypothesis testing : building a Bayesian A/B test --
Introduction to the Bayes factor and posterior odds : the competition of ideas --
Bayesian reasoning in the twilight zone --
When data doesn't convince you --
From hypothesis testing to parameter estimation --
Appendix A: A quick introduction to R --
Appendix B: Enough calculus to get by.
Responsibility: Will Kurt.
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A fun guide to learning Bayesian statistics and probability through unusual and illustrative examples.  Read more...


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