FreeComputerBooks.com
Links to Free Computer, Mathematics, Technical Books all over the World


 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
 Share This:
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
 Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference
 The Mirror Site (1)  PDF
 The Mirror Site (2)  PDF
 Book Homepage

O'Reilly® Think Bayes: Bayesian Statistics in Python
If you know how to program, you're ready to tackle Bayesian statistics. With this book, you'll learn how to solve statistical problems with Python code instead of mathematical formulas, using discrete probability distributions rather than continuous mathematics.

Bayesian Reasoning and Machine Learning (David Barber)
This practical introduction is ideally suited to computer scientists without a background in calculus and linear algebra. You'll develop analytical and problemsolving skills that equip them for the real world. Numerous examples and exercises are provided.

Bayesian Methods for Statistical Analysis (Borek Puza)
Bayesian methods for statistical analysis is a book on statistical methods for analysing a wide variety of data. It contains many exercises, all with worked solutions, including complete computer code.

Bayesian Data Analysis (Andrew Gelman, et al.)
This classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. It takes an applied approach to analysis using uptodate Bayesian methods.

An Introduction to Bayesian Thinking (Merlise Clyde, et al.)
This book provides an introduction to Bayesian inference in decision making without requiring calculus. It may be used on its own as an openaccess introduction to Bayesian inference using R for anyone interested in learning about Bayesian statistics.

Bayesian Networks and BayesiaLab (Stefan Conrady, et al.)
This practical introduction is geared towards scientists who wish to employ Bayesian Networks for applied research using the BayesiaLab software platform. It can serve as a selfstudy guide for learners and as a reference manual for advanced practitioners.
:






















