Probabilistic programming in practice
29.99 €
Out of stock
Probabilistic programming in practice
29.99 €
Probabilistic programming is a new way of creating probabilistic models that allow us to predict or infer new facts that are not present in observational results. This allows, for example, predicting future events such as sales trends, computing system failures, outcomes of experiments, and more.
The book is an introduction to probabilistic programming for practicing programmers. The author moves almost immediately to practical examples: building a spam filter, diagnosing errors in a computing system, and restoring digital images. You will be introduced to probabilistic inference, where algorithms help predict, for example, the use of social networks. Along the way, you'll learn about the use of functional programming style to analyze texts, object-oriented models to predict the distribution of tweets, and open-universe models to measure social network phenomena. The book also has chapters on how probabilistic models help in decision making and modeling dynamic systems. The data you collect about customers, products, and site users can assist not only in interpreting the past, but also in predicting the future. Summary: - Introduction to probabilistic modeling; - Writing probabilistic programs in Figaro; - Building Bayesian networks; - Predicting the product life cycle; - Decision algorithms.
The book is an introduction to probabilistic programming for practicing programmers. The author moves almost immediately to practical examples: building a spam filter, diagnosing errors in a computing system, and restoring digital images. You will be introduced to probabilistic inference, where algorithms help predict, for example, the use of social networks. Along the way, you'll learn about the use of functional programming style to analyze texts, object-oriented models to predict the distribution of tweets, and open-universe models to measure social network phenomena. The book also has chapters on how probabilistic models help in decision making and modeling dynamic systems. The data you collect about customers, products, and site users can assist not only in interpreting the past, but also in predicting the future. Summary: - Introduction to probabilistic modeling; - Writing probabilistic programs in Figaro; - Building Bayesian networks; - Predicting the product life cycle; - Decision algorithms.
See also:
- All books by the publisher
- All books by the author