Home | Amazing | Today | Tags | Publishers | Years | Account | Search 
Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference (Addison-Wesley Data & Analytics)

Buy

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 Davidson-Pilon introduces Bayesian inference from a computational perspective, bridging theory to practice–freeing 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. 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.

 

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 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

 

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.

(HTML tags aren't allowed.)

The Theory of Computation
The Theory of Computation

This is the best text on complexity theory I have seen, and could easily become the standard text on the subject...This is the first modern text on the theory of computing. ---William Ward Jr, Ph.D, University of South Alabama

Taking a practical approach, this modern introduction to the theory of computation focuses on the study of...

Introduction to Machine Learning with R: Rigorous Mathematical Analysis
Introduction to Machine Learning with R: Rigorous Mathematical Analysis

Machine learning is an intimidating subject until you know the fundamentals. If you understand basic coding concepts, this introductory guide will help you gain a solid foundation in machine learning principles. Using the R programming language, you’ll first start to learn with regression modelling and then move into more...

Deep Learning Cookbook: Practical Recipes to Get Started Quickly
Deep Learning Cookbook: Practical Recipes to Get Started Quickly

Deep learning doesn’t have to be intimidating. Until recently, this machine-learning method required years of study, but with frameworks such as Keras and Tensorflow, software engineers without a background in machine learning can quickly enter the field. With the recipes in this cookbook, you’ll learn how to solve...


Introduction to Automata Theory, Languages, and Computation (2nd Edition)
Introduction to Automata Theory, Languages, and Computation (2nd Edition)
It has been more than 20 years since this classic book on formal languages, automata theory, and computational complexity was first published. With this long-awaited revision, the authors continue to present the theory in a concise and straightforward manner, now with an eye out for the practical applications. They have revised this book to make it...
Essential Mathematics for Games and Interactive Applications: A Programmer's Guide
Essential Mathematics for Games and Interactive Applications: A Programmer's Guide
"Even though I've worked with these systems for years, I found new ways of looking at several topics that make them easier to remember and use. For someone new to 3D programming, it is extremely usefulit gives them a solid background in pretty much every area they need to understand." Peter Lipson, Toys for Bob, Inc.

Based on
...
Numerical Polynomial Algebra
Numerical Polynomial Algebra
This first book on the numerical analysis of polynomial systems is a stepping stone at the interface of symbolic computation and numerical computation. Bernard Sturmfels, Department of Mathematics, University of Berkeley

I am not familiar with any books that do such a careful job of combining numerical analysis with the algebra of
...
©2018 LearnIT (support@pdfchm.net) - Privacy Policy