TY  - BOOK
AU  - Koduvely,Hari M.
TI  - Learning Bayesian models with R: become an expert in Bayesian machine learning methods using R and apply them to solve real-world big data problems 
T2  - Community experience distilled
SN  - 9781783987610
AV  - QA76.9.Q36 
U1  - 005.13/3 23
PY  - 2015///
CY  - Birmingham, UK
PB  - Packt Publishing
KW  - Machine learning
KW  - Quantitative research
KW  - R (Computer program language)
KW  - Apprentissage automatique
KW  - Recherche quantitative
KW  - R (Langage de programmation)
KW  - COMPUTERS
KW  - Programming Languages
KW  - General
KW  - bisacsh
KW  - fast
KW  - Electronic books
N1  - Includes index; Includes bibliographical references and index
N2  - Become an expert in Bayesian Machine Learning methods using R and apply them to solve real-world big data problems About This Book Understand the principles of Bayesian Inference with less mathematical equations Learn state-of-the art Machine Learning methods Familiarize yourself with the recent advances in Deep Learning and Big Data frameworks with this step-by-step guide 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. 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 In Detail Bayesian Inference provides a unified framework to deal with all sorts of uncertainties when learning patterns form data using machine learning models and use it for predicting future observations. However, learning and implementing Bayesian models is not easy for data science practitioners due to the level of mathematical treatment involved. Also, applying Bayesian methods to real-world problems requires high computational resources. With the recent advances in computation and several open sources packages available in R, Bayesian modeling has become more feasible to use for practical applications today. Therefore, it would be advantageous for all data scientists and engineers to understand Bayesian methods and apply them in their projects to achieve better results. Learning Bayesian Models with R starts by giving you a comprehensive coverage of the Bayesian Machine Learning models and the R packages that implement them. It begins with an introduction to the fundamentals of probability theory and R programming for those who are new to
UR  - https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1087967
ER  -