num_pages: 256
description: Master Bayesian Inference through Practical Examples and ComputationWithout 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 Davidson-Pilon introduces Bayesian inference from a computational perspective bridging theory to practicefreeing you to get results using computing power. 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. 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. Youll 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 youve mastered these techniques youll constantly turn to this guide for the working PyMC code you need to jumpstart future projects. Coverage includes  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 models 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 estimates 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 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 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.
language: English
title: Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference
publisher: Addison-Wesley Professional