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


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.)

Network Security Through Data Analysis: From Data to Action
Network Security Through Data Analysis: From Data to Action

Traditional intrusion detection and logfile analysis are no longer enough to protect today’s complex networks. In the updated second edition of this practical guide, security researcher Michael Collins shows InfoSec personnel the latest techniques and tools for collecting and analyzing network traffic datasets. You’ll...

Mathematical Methods for Knowledge Discovery and Data Mining
Mathematical Methods for Knowledge Discovery and Data Mining
The importance of knowledge discovery and data mining is evident by the great plethora of books and papers dedicated to this subject. Such methods are finding applications in almost any area of human endeavor. This includes applications in engineering, science, business, medicine, humanities, just to name a few. At the same time, however, there is...
IT Security Risk Control Management: An Audit Preparation Plan
IT Security Risk Control Management: An Audit Preparation Plan

Follow step-by-step guidance to craft a successful security program. You will identify with the paradoxes of information security and discover handy tools that hook security controls into business processes.

Information security is more than configuring firewalls, removing viruses, hacking machines, or setting passwords. Creating...

Developing Microsoft Visio solutions
Developing Microsoft Visio solutions

Developing Microsoft Visio Solutions is a complete guide to creating solutions with Microsoft Visio. This guide presents:

  • An introduction to the Visio environment and conceptual information about Developing Microsoft Visio Solutions.
  • Detailed information about using formulas to design SmartShapes...
The Art of Unit Testing: with examples in C#
The Art of Unit Testing: with examples in C#


The Art of Unit Testing, Second Edition guides you step by step from writing your first simple tests to developing robust test sets that are maintainable, readable, and trustworthy. You'll master the foundational ideas and quickly move to high-value subjects like mocks, stubs, and isolation,...

Cyber Security Policy Guidebook
Cyber Security Policy Guidebook

Drawing upon a wealth of experience from academia, industry, and government service, Cyber Security Policy Guidebook details and dissects, in simple language, current organizational cyber security policy issues on a global scale—taking great care to educate readers on the history and current approaches to the security of...

©2018 LearnIT (support@pdfchm.net) - Privacy Policy