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R Statistics Cookbook

You're reading from   R Statistics Cookbook Over 100 recipes for performing complex statistical operations with R 3.5

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Product type Paperback
Published in Mar 2019
Publisher Packt
ISBN-13 9781789802566
Length 448 pages
Edition 1st Edition
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Author (1):
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Francisco Juretig Francisco Juretig
Author Profile Icon Francisco Juretig
Francisco Juretig
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Table of Contents (12) Chapters Close

Preface 1. Getting Started with R and Statistics FREE CHAPTER 2. Univariate and Multivariate Tests for Equality of Means 3. Linear Regression 4. Bayesian Regression 5. Nonparametric Methods 6. Robust Methods 7. Time Series Analysis 8. Mixed Effects Models 9. Predictive Models Using the Caret Package 10. Bayesian Networks and Hidden Markov Models 11. Other Books You May Enjoy

Introduction

In this chapter, we present several Bayesian techniques in R, using either STAN or JAGS (both are the most important software packages that can be used in R). Bayesian statistics is fundamentally different from classical statistics. In the latter, parameters are fixed quantities that need to be found. In the Bayesian framework, parameters are random variables themselves that can be learned. Furthermore, Bayesian statistics allows us to incorporate prior knowledge about a distribution that we want to learn, and update it accordingly.

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