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Product details
File Size: 22321 KB
Print Length: 487 pages
Simultaneous Device Usage: Up to 4 simultaneous devices, per publisher limits
Publisher: Chapman and Hall/CRC; 1 edition (January 3, 2018)
Publication Date: January 3, 2018
Sold by: Amazon Digital Services LLC
Language: English
ASIN: B078SDGFBW
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Amazon Best Sellers Rank:
#86,735 Paid in Kindle Store (See Top 100 Paid in Kindle Store)
This book is unbelievably great. I don't have a great math background but I do have a significant programming background, so understanding algorithms in terms of code is always much easier than trying to decipher the math; so since this book mostly focuses on code, it makes it that much easier. But more importantly than that, this book really attempts (and succeeds) to give an intuitive understanding of all the concepts rather than delivering a protocol for performing Bayesian analysis. I've read most of Kruschke's "Doing Bayesian Analysis" and while that book is perhaps more comprehensive in what it covers, and arguably has better graphics, this book blows it out of the water (and it's like half the length). Reading other Bayesian statistics books and documents made me think "I kind of get it" but after reading this book everything just clicked. I not only understand the basic procedures of Bayesian analysis but the underlying reasons as to where all of this came from and why we do things the way we do. The author uses clear down-to-earth examples to illustrate all major concepts and avoids or clearly explains any technical jargon making this perhaps THE most accessible book on Bayesian analysis on the market. He also knows where to dive into details and where abstracting a bit is most appropriate. There's also little in situ boxes ("Overthinking") with optional information if one wants to know more details about the current topic. Moreover, the "rethinking" R package that is used in the book is great. It comes with very useful helper functions to focus on learning concepts rather than wasting time explaining code minutiae, and it also has built in data sets for practice that are great.I've personally spent a good deal of time researching and reading and I can unequivocally say this is the best book for anyone wanting to get started with Bayesian data analysis who has at least basic programming skills and a basic understanding of calculus. This book is not only a great learning resource, it's actually fun to read. The only downside to this book is that it is very expensive. It's definitely worth it if you're serious about learning Bayesian data analysis but I wish the book didn't have to be so expensive. I will say that the paper and binding seems high-quality though.All in all I would say this is probably the most satisfied I've been with a book purchase in recent history. I cannot recommend this highly enough. I wish all math/programing/statistics books were this good. The manner and quality of teaching really makes all the difference.***Edit ( 25 April 2018 ): I purchased this book about a year and a half ago and I read the whole thing and did most of the exercises back then. I recently picked up the book again because I wanted to do run a particular Bayesian analysis but forgot the details. One con (perhaps the only significant con) of the book is that the index in the back is pretty terrible, it doesn't index nearly enough of the words or terms, so I ended up just starting at page 1 just to refresh my memory of where to look, and I got hooked all over again. I can't believe I'm re-reading this book from the beginning again but I am because it's that good. In fact, I had forgotten I that I had reviewed this book so I came here to write a raving review and saw that I had already written one, so instead I'm just updating it to demonstrate that this book was not only great the first time I read it, but it stands the test of time.
I don't usually comment, but I just wanted to say the book is outstanding.It is very accessible -there is barely any math- and focuses on how to connect the principle behind each theory to its potential application, emphasizing scope, limitations and philosophy. I helps you to think statistically. On top of that, it shows you the applications with programming and coding.Although I had already a decent training in stats, up to a first year of grad school, I found this book enlightening, full of insights, fun to read. There were many issues I already knew, but that I had not connected together. Definitely the best book I know -and I believe I know insanely many books on the topic- you can choose for an intermediate class on data analysis.
This is the book that I wish I had read first when learning Bayesian statistics. The highlights are:1. It starts with a stepped-through example explaining how to link priors, calculate likelihood and arrive at a posterior, all using a grid/matrix approach. Whilst not something one would normally do in real analysis, the basic concept is wonderfully illustrated here. It provides rock solid foundations for the later material.2. Throughout the book there is R-code, along with a functioning download. The reader can try, fiddle, break and repair examples as much as need be to understand the concepts.3. There is an excellent series of Youtube recordings to assist in understanding (highly recommended).4. The end result is being able to use Stan, only utilising the rethinking package to implement models. The alternative to this approach is either to learn Stan (which is not hard, but subsequent to this book) or to use rstanarm (in CRAN). Both these options are more likely effective alternatives AFTER learning to use the rethinking package.5. The focus on plotting and the useful wrappers to do so in the rethinking package quickly get the reader to a point of being able to use real data of relevance to him or her.6. The wrap-up for the book is around GLM models and how these might productively be applied in research settings.7. The focus on social sciences also is highly relevant given the controversies around some statistical procedures. Optimistically, this text offers a solid way forward for a graduate student into a research dissertation using Bayesian methods.8. The rethinking package includes datasets to use in developing one's skills from this text.What are the limitations? Possibly, it might have been useful to have greater clarity around how the wrappers work (e.g., map, map2stan). But even there, one can inspect the code, if required. Also, whilst I personally find the rethinking package to be quite brilliantly useful, compared to rstanarm, it is not clear whether it will remain current. This is more a gripe about R, which unlike Python, seems to believe that there a many many ways to construct good code, rather than one, best way.I unreservedly recommend this text as a start and intermediate development point for an applied user of HMC Bayesian methods using Stan.
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