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Master Bayesian Inference through Practical Examples and Computation–Without Advanced Mathematical Analysis
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Bayesian methods of inference are deeply natural and extremely powerful. However, most discussions of Bayesian inference rely on intensely complex mathematical analyses and artificial examples, making it inaccessible to anyone without a strong mathematical background. Now, though, Cameron Davidson-Pilon introduces Bayesian inference from a computational perspective, bridging theory to practice–freeing you to get results using computing power.
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Bayesian Methods for Hackers illuminates Bayesian inference through probabilistic programming with the powerful PyMC language and the closely related Python tools NumPy, SciPy, and Matplotlib. Using this approach, you can reach effective solutions in small increments, without extensive mathematical intervention.
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Davidson-Pilon begins by introducing the concepts underlying Bayesian inference, comparing it with other techniques and guiding you through building and training your first Bayesian model. Next, he introduces PyMC through a series of detailed examples and intuitive explanations that have been refined after extensive user feedback. You’ll learn how to use the Markov Chain Monte Carlo algorithm, choose appropriate sample sizes and priors, work with loss functions, and apply Bayesian inference in domains ranging from finance to marketing. Once you’ve mastered these techniques, you’ll constantly turn to this guide for the working PyMC code you need to jumpstart future projects.
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Coverage includes
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• Learning the Bayesian “state of mind” and its practical implications
• Understanding how computers perform Bayesian inference
• Using the PyMC Python library to program Bayesian analyses
• Building and debugging models with PyMC
• Testing your model’s “goodness of fit”
• Opening the “black box” of the Markov Chain Monte Carlo algorithm to see how and why it works
• Leveraging the power of the “Law of Large Numbers”
• Mastering key concepts, such as clustering, convergence, autocorrelation, and thinning
• Using loss functions to measure an estimate’s weaknesses based on your goals and desired outcomes
• Selecting appropriate priors and understanding how their influence changes with dataset size
• Overcoming the “exploration versus exploitation” dilemma: deciding when “pretty good” is good enough
• Using Bayesian inference to improve A/B testing
• Solving data science problems when only small amounts of data are available
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Cameron Davidson-Pilon has worked in many areas of applied mathematics, from the evolutionary dynamics of genes and diseases to stochastic modeling of financial prices. His contributions to the open source community include lifelines, an implementation of survival analysis in Python. Educated at the University of Waterloo and at the Independent University of Moscow, he currently works with the online commerce leader Shopify.
- Sales Rank: #269843 in Books
- Published on: 2015-10-12
- Original language: English
- Number of items: 1
- Dimensions: 9.00" h x .50" w x 7.00" l, .0 pounds
- Binding: Paperback
- 256 pages
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.
Most helpful customer reviews
25 of 25 people found the following review helpful.
Bingo
By Dimitri Shvorob
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.
2 of 2 people found the following review helpful.
A very useful reference
By David S. Saunders
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.
3 of 4 people found the following review helpful.
"Null-hypothesis tests are not completely stupid, but Bayesian statistics are better." (Rindskopf, 1998)
By Ramin Madarshahian
This book is very good for engineering field who want to apply Bayesian method. I do not recommend it for statistician, unless they just want to learn how work with pymc package of python.
This book really helped me to learn concept of modeling and Bayesian programming. I am very glad to have it.
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