Learning Bayesian Models with R

Key Features

Book Description

What you will learn

• Set up the R environment
• Create a classification model to predict and explore discrete variables
• Get acquainted with Probability Theory to analyze random events
• Build Linear Regression models
• Use Bayesian networks to infer the probability distribution of decision variables in a problem
• Model a problem using Bayesian Linear Regression approach with the R package BLR
• Use Bayesian Logistic Regression model to classify numerical data
• Perform Bayesian Inference on massively large data sets using the MapReduce programs in R and Cloud computing

Who this book is for

This book is for statisticians, analysts, and data scientists who want to build a Bayes-based system with R and implement it in their day-to-day models and projects. It is mainly intended for Data Scientists and Software Engineers who are involved in the development of Advanced Analytics applications. To understand this book, it would be useful if you have basic knowledge of probability theory and analytics and some familiarity with the programming language R.