Practical Probabilistic Programming Free Download
Category: Internet Guides & Online Services|
Format files: PDF, EPUB, TXT, DOC
The size of the: 444 KB
City - Country: New York, United States
The author of the book: Ava Pfeffer
Edition: Manning Publications
Date of issue: 10 Apr 2016
Full description of the book "Practical Probabilistic Programming":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 PDF 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 ePub 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 PDF in day-to-day work as a data scientist.
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