Practical Probabilistic Programming | 0:e upplagan

DESCRIPTION

Data accumulated about customers, products, and website users can not only help interpret the past, it can help predict the future! Probabilistic programming is a programming paradigm in which code models are used to draw probabilistic inferences from data. By applying specialized algorithms, programs assign degrees of probability to conclusions and make it possible to forecast future events like sales trends, computer system failures, experimental outcomes, and other critical concerns.

 

Practical Probabilistic Programming explains how to use the PP paradigm to model application domains and express those probabilistic models in code. It shows how to use the Figaro language to build a spam filter and apply Bayesian and Markov networks to diagnose computer system data problems and recover digital images. Then it dives into the world of probabilistic inference, where algorithms help turn the extended prediction of social media usage into a science. The book covers functional-style programming for text analysis and using object-oriented models to predict social phenomena like the spread of tweets, and using open universe models to model real-life social media usage. It also teaches the principles of algorithms such as belief propagation and Markov chain Monte Carlo. The book closes out with modeling dynamic systems by using a product cycle as its main example and explains how probabilistic

 

KEY SELLING POINTS

Covers the basic rules of probabilistic inference

Illustrated with useful practical examples

Build a wide variety of probabilistic models

 

AUDIENCE

Code examples are written in Figaro. Some knowledge of Scala and a basic foundation in data science is helpful. No prior exposure to probabilistic programming is required.

 

ABOUT THE TECHNOLOGY

Probabilistic programming is a new discipline, and the tools and best practices are still emerging. Powerful new tools like the Figaro library built into Scala make probabilistic programming more practical in day-to-day work as a data scientist.