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Learning Bayesian Models with R, by Dr. Hari M. Koduvely
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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
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
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 the subject. Then the book covers some of the important machine learning methods, both supervised and unsupervised learning, implemented using Bayesian Inference and R.
Every chapter begins with a theoretical description of the method explained in a very simple manner. Then, relevant R packages are discussed and some illustrations using data sets from the UCI Machine Learning repository are given. Each chapter ends with some simple exercises for you to get hands-on experience of the concepts and R packages discussed in the chapter.
The last chapters are devoted to the latest development in the field, specifically Deep Learning, which uses a class of Neural Network models that are currently at the frontier of Artificial Intelligence. The book concludes with the application of Bayesian methods on Big Data using the Hadoop and Spark frameworks.
Style and approachThe book first gives you a theoretical description of the Bayesian models in simple language, followed by details of its implementation in the R package. Each chapter has illustrations for the use of Bayesian model and the corresponding R package, using data sets from the UCI Machine Learning repository. Each chapter also contains sufficient exercises for you to get more hands-on practice.
- Sales Rank: #1842495 in Books
- Published on: 2015-10-28
- Released on: 2015-10-28
- Original language: English
- Number of items: 1
- Dimensions: 9.25" h x .38" w x 7.50" l, .66 pounds
- Binding: Paperback
- 168 pages
About the Author
Dr. Hari M. Koduvely
Dr. Hari M. Koduvely is an experienced data scientist working at the Samsung R&D Institute in Bangalore, India. He has a PhD in statistical physics from the Tata Institute of Fundamental Research, Mumbai, India, and post-doctoral experience from the Weizmann Institute, Israel, and Georgia Tech, USA. Prior to joining Samsung, the author has worked for Amazon and Infosys Technologies, developing machine learning-based applications for their products and platforms. He also has several publications on Bayesian inference and its applications in areas such as recommendation systems and predictive health monitoring. His current interest is in developing large-scale machine learning methods, particularly for natural language understanding.
Most helpful customer reviews
7 of 8 people found the following review helpful.
Not sure about this one
By Dimitri Shvorob
Let me be frank: I don't like Packt, a publisher that saves money on editing and graphic design, and just keeps churning out un-edited, ugly books by authors who could not, or did not, go with a proper publisher. They give free e-books to reviewers, and many feel obliged to return the favor by posting super-positive, but detail-free "reviews", which don't mention any alternatives, and sometimes name-check a book's key terms, but in an odd way that suggests that they don't really know what those mean. Just look at the reviews, and ratings, of this book's fan Dipanjan Sarkar, for example. I have seen many Packt books, and many Dipanjans, and I am annoyed.
Anyway, this is not a five-star book. It is not a typical Packt book, in that Packt publishes IT books, and this is a formula-ridden statistics book that would be more at home in the catalog of an academic publisher like Springer or CRC-Hall. It starts with a concise if dry survey of Bayesian basics, and then surveys several Bayesian methods, implemented by specific R packages - "arm", "BayesLogit", "lda", "brnn", "bgmm", "darch". I see good things about the first part; the second one, on the other hand, came across as not very clearly written and too superficial to be useful: for example, I simply failed to understand the author's explanation of the Bayesian logit, and that should not have been complicated. The decision to go with specific R packages is understandable, but the failure to even mention BUGS, JAGS or STAN - the popular, general tools - is not.
I don't understand who the book is for. The people who can handle the integrals, so to speak, will find the relevant R packages on their own. The less technical readers, on the other hand, will be put off by the academic style. My suggestion to the latter is "Doing Bayesian Data Analysis" by Kruschke. There is also a good, accessible book which uses Python rather than R, by Davidson-Pilon.
UPD. With the benefit of a little more life experience, I would say: don't spend your time on *any* R book. Python is the way to go.
0 of 0 people found the following review helpful.
Strong basis for best solutions!
By Hugo
This book is good, have a clear reading, structured information in good steps, exercises and references, these last two are very useful when you want more detailed information. Statistics aren't easy at least for me, but I could learn the advantages of Bayesian inference.
I think that the first chapters are essential introduction to the subject and the tools to work, but, the real what you really want comes in modules, first you have an understatement of the use and capabilities of principles of Bayesian inference, after that you have notion of Bayesian and R, than you start to use both in machine learning. Machine Learning have many uses, so I think that the applicability of the book tend to infinity, I really liked that the author gives base of Bayesian neural networks in chapter 8, talking about deep belief networks the advantages and like in all the other subjects he gives good references to go deep and learn for sure. You will understand wow structured is the book when you achieve the last chapter and see how much you've learned and that the complexity of your projects achieve, all the chapters are like a stair degree.
My experience reading this was good because I feel that I've learned and the exercises make me work with, Bayesian inference is different from classic statistics, you can you this to solve yor project needs, I certainly recommend this book, is hard to find such information well explained like in this book.
2 of 2 people found the following review helpful.
but when I compare the actual predictions with reference values the Bayesian model actually performs marginally worse than ordin
By Vincent
The book provides a quick review of all the main things you need to know when running Bayesian analyses in R. It will not make you a Bayesian wizard, but it could serve as a quick introduction to Bayesian analyses in R.
A point of critic is that at some places I felt that the author could have provided a bit more guidance on interpretation. For example, chapter 5 explains how to fit a Bayesian model and how to simulate the posterior distribution, but does not devote a single line to explain how a user should interpret and use that simulation compared with the model coefficients. Further, chapter 5 states that smaller confidence intervals in the Bayesian model is a major benefit, but when I compare the actual predictions with reference values the Bayesian model actually performs marginally worse than ordinary least square regression. The author should really do more effort to explain why smaller confidence intervals are worth the reduction in actual model quality.
Code examples are simply a log of the command line entries the author, including odd repetitions. Further, the code has some poor programming habits, e.g. using the attach(data) function is not meaningful if you already pass the data argument to the model.
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