{"product_id":"9781617292330","title":"Practical Probabilistic Programming","description":"\u003cp\u003e\u003cb\u003eSummary\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003e\u003ci\u003ePractical Probabilistic Programming\u003c\/i\u003e introduces the working programmer to probabilistic programming. In it, you'll learn how to use the PP paradigm to model application domains and then express those probabilistic models in code. Although PP can seem abstract, in this book you'll immediately work on practical examples, like using the Figaro language to build a spam filter and applying Bayesian and Markov networks, to diagnose computer system data problems and recover digital images. \u003c\/p\u003e\u003cp\u003ePurchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eAbout the Technology\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003eThe data you accumulate about your customers, products, and website users can help you not only to interpret your past, it can also help you predict your future! Probabilistic programming uses code to draw probabilistic inferences from data. By applying specialized algorithms, your programs assign degrees of probability to conclusions. This means you can forecast future events like sales trends, computer system failures, experimental outcomes, and many other critical concerns. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eAbout the Book\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003e\u003ci\u003ePractical Probabilistic Programming\u003c\/i\u003e introduces the working programmer to probabilistic programming. In this book, you’ll immediately work on practical examples like building a spam filter, diagnosing computer system data problems, and recovering digital images. You’ll discover probabilistic inference, where algorithms help make extended predictions about issues like social media usage. Along the way, you’ll learn to use functional-style programming for text analysis, object-oriented models to predict social phenomena like the spread of tweets, and open universe models to gauge real-life social media usage. The book also has chapters on how probabilistic models can help in decision making and modeling of dynamic systems. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eWhat's Inside\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eIntroduction to probabilistic modeling\u003c\/li\u003e\n\u003cli\u003eWriting probabilistic programs in Figaro\u003c\/li\u003e\n\u003cli\u003eBuilding Bayesian networks\u003c\/li\u003e\n\u003cli\u003ePredicting product lifecycles\u003c\/li\u003e\n\u003cli\u003eDecision-making algorithms\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003e\u003cb\u003eAbout the Reader\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003eThis book assumes no prior exposure to probabilistic programming. Knowledge of Scala is helpful. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eAbout the Author\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eAvi Pfeffer\u003c\/b\u003e is the principal developer of the Figaro language for probabilistic programming. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003col\u003e\n\u003cb\u003ePART 1 INTRODUCING PROBABILISTIC PROGRAMMING AND FIGARO\u003c\/b\u003e\u003cli\u003eProbabilistic programming in a nutshell \u003c\/li\u003e\n\u003cli\u003eA quick Figaro tutorial \u003c\/li\u003e\n\u003cli\u003eCreating a probabilistic programming application \u003c\/li\u003e\n\u003cb\u003ePART 2 WRITING PROBABILISTIC PROGRAMS\u003c\/b\u003e\u003cli\u003eProbabilistic models and probabilistic programs \u003c\/li\u003e\n\u003cli\u003eModeling dependencies with Bayesian and Markov networks \u003c\/li\u003e\n\u003cli\u003eUsing Scala and Figaro collections to build up models \u003c\/li\u003e\n\u003cli\u003eObject-oriented probabilistic modeling \u003c\/li\u003e\n\u003cli\u003eModeling dynamic systems \u003c\/li\u003e\n\u003cb\u003ePART 3 INFERENCE\u003c\/b\u003e\u003cli\u003eThe three rules of probabilistic inference \u003c\/li\u003e\n\u003cli\u003eFactored inference algorithms \u003c\/li\u003e\n\u003cli\u003eSampling algorithms \u003c\/li\u003e\n\u003cli\u003eSolving other inference tasks \u003c\/li\u003e\n\u003cli\u003eDynamic reasoning and parameter learning\u003c\/li\u003e\n\u003c\/ol\u003e","brand":"Manning Publications Company","offers":[{"title":"Default Title","offer_id":47056519037168,"sku":"9781617292330","price":59.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0737\/7593\/9824\/files\/9781617292330_p0.jpg?v=1763853944","url":"https:\/\/shop-qa.barnesandnoble.com\/products\/9781617292330","provider":"Barnes \u0026 Noble (DEV)","version":"1.0","type":"link"}