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 Title Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference Using Python and PyMC
 Author(s) Cameron DavidsonPilon
 Publisher: AddisonWesley Professional (October 12, 2015); eBook (Online Edition. Updated Continuously)
 License(s): MIT License
 Paperback 256 pages
 eBook Jupyter Notebooks
 Language: English
 ISBN10/ASIN: 0133902838
 ISBN13: 9780133902839
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Book Description
Master Bayesian Inference through Practical Examples and Computation  Without Advanced Mathematical Analysis.
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 DavidsonPilon introduces Bayesian inference from a computational perspective, bridging theory to practiceâ€“freeing you to get results using computing power.
This book 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.
This book is designed as an introduction to Bayesian inference from a computational understandingfirst, and mathematicssecond, point of view. The book assumes no prior knowledge of Bayesian inference nor probabilistic programming.
About the Authors Cameron DavidsonPilon 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 opensource 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.
 Bayesian Thinking
 Statistics, Mathematical Statistics
 Probability and Stochastic
 Python Programming
 Computational and Algorithmic Mathematics