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R Statistics Cookbook

You're reading from  R Statistics Cookbook

Product type Book
Published in Mar 2019
Publisher Packt
ISBN-13 9781789802566
Pages 448 pages
Edition 1st Edition
Languages
Concepts
Author (1):
Francisco Juretig Francisco Juretig
Profile icon Francisco Juretig

Table of Contents (12) Chapters

Preface Getting Started with R and Statistics Univariate and Multivariate Tests for Equality of Means Linear Regression Bayesian Regression Nonparametric Methods Robust Methods Time Series Analysis Mixed Effects Models Predictive Models Using the Caret Package Bayesian Networks and Hidden Markov Models 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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