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Avi Pfeffer - Practical Probabilistic Programming [2016, PDF, ENG]

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Post 02-May-2016 00:03

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Practical Probabilistic Programming
Год издания: 2016
Автор: Avi Pfeffer
Издательство: Manning Publications
ISBN: 9781617292330
Язык: Английский
Формат: PDF
Качество: Издательский макет или текст (eBook)
Интерактивное оглавление: Да
Количество страниц: 456
Описание: Practical Probabilistic Programming 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.
What's Inside
- Introduction to probabilistic modeling
- Writing probabilistic programs in Figaro
- Building Bayesian networks
- Predicting product lifecycles
- Decision-making algorithms

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Оглавление

brief contents
PART 1 INTRODUCING PROBABILISTIC PROGRAMMING
AND FIGARO. ................................................................1
1 ■ Probabilistic programming in a nutshell 3
2 ■ A quick Figaro tutorial 27
3 ■ Creating a probabilistic programming application 57
PART 2 WRITING PROBABILISTIC PROGRAMS ............................91
4 ■ Probabilistic models and probabilistic programs 93
5 ■ Modeling dependencies with Bayesian and
Markov networks 129
6 ■ Using Scala and Figaro collections to build up models 172
7 ■ Object-oriented probabilistic modeling 200
8 ■ Modeling dynamic systems 229
PART 3 INFERENCE. ..............................................................255
9 ■ The three rules of probabilistic inference 257
10 ■ Factored inference algorithms 283
11 ■ Sampling algorithms 321
12 ■ Solving other inference tasks 360
13 ■ Dynamic reasoning and parameter learning 382
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