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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

This chapter discusses two powerful techniques used in advanced statistics. The first one, Bayesian networks (usually referred to as BNs), is a graphical model based on Bayesian theory, which is used to represent probabilistic relationships between several variables. The second one, the hidden Markov model (HMM), is a model that can handle both observable and non-observable variables that affect a dependent variable. In the simplest scenario, we might observe whether people arrive at an office with an umbrella or not, with the intention of deducing whether it was raining or not.

This chapter is divided into two parts: there are four recipes dedicated to BNs and three recipes dedicated to HMMs.

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