Download PDF Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference (Addison-Wesley Data & Analytics)

Download PDF Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference (Addison-Wesley Data & Analytics)

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Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference (Addison-Wesley Data & Analytics)

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About the Author

Cameron Davidson-Pilon has seen many fields of applied mathematics, from evolutionary dynamics of genes and diseases to stochastic modeling of financial prices. His main contributions to the open-source community include Bayesian Methods for Hackers and lifelines. Cameron was raised in Guelph, Ontario, but was educated at the University of Waterloo and Independent University of Moscow. He currently lives in Ottawa, Ontario, working with the online commerce leader Shopify.

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Product details

Series: Addison-Wesley Data & Analytics

Paperback: 256 pages

Publisher: Addison-Wesley Professional; 1 edition (October 12, 2015)

Language: English

ISBN-10: 9780133902839

ISBN-13: 978-0133902839

ASIN: 0133902838

Product Dimensions:

6.9 x 0.5 x 8.9 inches

Shipping Weight: 9.1 ounces (View shipping rates and policies)

Average Customer Review:

4.0 out of 5 stars

20 customer reviews

Amazon Best Sellers Rank:

#51,436 in Books (See Top 100 in Books)

This book, along with Think Stats: Exploratory Data Analysis,Think Bayes: Bayesian Statistics in Python, and Bayes' Rule: A Tutorial Introduction to Bayesian Analysis, improved my understanding for the motivations, applications, and challenges in Bayesian statistics and probabilistic programming. For reference, my background is in computer science, viewed mostly from a software engineering perspectiveWhen you consider that this book is offered for free on Github, you're already getting a lot of value for free. If you're interested in seeing the exercise solutions (there are only a handful) or the additional chapter on A/B testing, then $17 for the e-book is a fair price.My only complaint is that I wish there were more exercises to test the reader along the way. You can re-implement many of the examples yourself, but having a few more questions, especially in the later chapters would be helpful for validating understanding.

There's a lot in here and, clearly, the author knows what he's talking about. However, it helps if you already have a background in statistics. Specifically, if you already know about prior and posterior probabilities you will have an easier time understanding this book. Installing PyMC under Windows can be challenging. It's not as simple as "pip install pymc". You might have to install the GCC with Windows specific libraries. Also, there might be an error on pg. 182 in Chapt. 6 - Getting our Priorities Straight (good sense of humour). The analysis that "TSLA is a strong performer" might be incorrect. I tried emailing the author but got no response on this.All that being said, this is a good book with a lot of information in just over 200 pages.

Great open source book, hard copy was definitely worth the buy.

This is a very informative guide to thinking about programming from a Bayesian point of view. The word hackers in the title may be misleading to some, but if you think about hackers as explorers, builders and people who like to figure out how things, work, this is an approach to reason and thinking that can open new doors to a "hacker." The math can get a little funky at times, but that's a problem for you just power through and keep reading because the math is there to help illustrate the approaches and isn't specifically required for all of the exercises.

Good book; an updated pymc3 version is available online (for free), but I have found pymc (pymc2) is better for learning MCMC. Recommended.

I really like the book, but want to bring up two things neglected by its author.One is acknowledgments. "Bayesian Methods For Hackers" did not appear in a vacuum. I would like to see a hat tip to the creators of PyMC, and at least a mention of BUGS, the still-very-much-alive software which brought Bayesian methods to academic masses and inspired MCMC-engine projects like PyMC. Then there are PyMC's cousins JAGS and STAN - these can be familiar to the R crowd - and people who wrote popular books on Bayesian analysis, such as John Kruschke, the author of "Doing Bayesian Data Analysis". (I should also mention James Stone, with his "Bayes' Rule". "Bayesian Modeling Using WinBUGS" by Ioannis Ntzoufras could have had more impact, but its publisher, Wiley, sabotaged the book with greedy pricing and no-frills presentation. Looking into the near future - I see that Manning have their own "probabilistic programming" book in the works, by Avi Pfeiffer). Naming those people, programs and books would provide useful pointers to aspiring "Bayesian hackers".The second reservation is about editorial effort. The very first page (when it used "ascribe" instead of "subscribe") told me that the manuscript had not been proof-read. As I went through the book, I found more unpolished passages, and a handful of lines capable of triggering a facepalm by a statistics professor. Now, this is not a book for statistics professors - they don't tend to use Python, for starters, while BMH assumes that you have Python installed - and the practical question is whether these issues are going to seriously frustrate or mislead the average reader. My answer is "not really". Chapter 3, for example, is dodgy, yet realistically a "hacker" can just skip or skim it. (Kudos on page 77, by the way - simple and effective). Classical, non-Bayesian statistics misunderstood and unfairly maligned? Only a stats prof will care. I think you can get through smaller slip-ups. For example, on page 15, you can quickly figure out that "lambda_" is a vector - unlike "lambda_1" and "lambda_2", which are scalar parameters - and understand what it's for, even though the author is just telling you that it's a function. Or, on page 142, you can figure out what loss function is implemented in stock_loss(), even though the text does not tell you what it is. Page 42 asks "Why is the gamma axis greater than 1?" You realize that it's Y, not gamma, and the question is asking whether the PDF of a continuous distribution can be above 1, and move on.I find these complaints easy to overlook. Thinking "big picture", I see the first-ever accessible, engaging, visually appealing, practical, inexpensive introduction to Bayesian methods. Echoing - now in a positive way - the beginning of my review, there is a feeling of continuity, of a long-running collective project being advanced. First, a user-friendly MCMC engine is built by the BUGS team. Then, the PyMC team incorporates their MCMC engine into a programming language popular with the "data science" crowd, sprinkling Pythonesque syntax sugar over the BUGS model definition language. Finally, after a few years, a good book on PyMC shows up. Bayesian methods are now accessible to a broad, non-academic audience.American Statistical Association - give this man a medal.

No problems

A brilliant and practical introduction to these methods. It's been a vital entry point for me into the field.

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