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 practiceтАУfreeing 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. YouтАЩll 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 youтАЩve mastered these techniques, youтАЩll constantly turn to this guide for the working PyMC code you need to jumpstart future projects.
Coverage includes
тАв Learning the Bayesian тАЬstate of mindтАЭ and its practical implications
тАв Understanding how computers perform Bayesian inference
тАв Using the PyMC Python library to program Bayesian analyses
тАв Building and debugging models with PyMC
тАв Testing your modelтАЩs тАЬgoodness of fitтАЭ
тАв Opening the тАЬblack boxтАЭ of the Markov Chain Monte Carlo algorithm to see how and why it works
тАв Leveraging the power of the тАЬLaw of Large NumbersтАЭ
тАв Mastering key concepts, such as clustering, convergence, autocorrelation, and thinning
тАв Using loss functions to measure an estimateтАЩs weaknesses based on your goals and desired outcomes
тАв Selecting appropriate priors and understanding how their influence changes with dataset size
тАв Overcoming the тАЬexploration versus exploitationтАЭ dilemma: deciding when тАЬpretty goodтАЭ is good enough
тАв Using Bayesian inference to improve A/B testing
тАв 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.
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.