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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

Assigning the priors

As we know, the priors are ingested by the MCMC algorithm, and are used to calculate the posterior densities. But how should the priors be assigned? Do we actually need a prior for each parameter?

Defining the support

Priors are just statistical distributions that reflect the initial expectation that the modeler has about each parameter. The very first thing we need to decide is, what is the support for the corresponding distributions? For example, for most coefficients in a linear regression model, the modeler very likely knows the correct sign for them. When modeling sales of a product in terms of its price and a promotional effect, the price effect should be negative (a higher price = less sales), and...

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