Practical Probabilistic Programming introduces the operating programmer to probabilistic programming. In it, you will the right way to use the PP paradigm to version software domain names after which exhibit these probabilistic types in code. even if PP can appear summary, during this booklet you will instantly paintings on functional examples, like utilizing the Figaro language to construct a junk mail clear out and utilising Bayesian and Markov networks, to diagnose machine process info difficulties and recuperate electronic photos.
Purchase of the print ebook encompasses a unfastened publication in PDF, Kindle, and ePub codecs from Manning Publications.
About the Technology
The information you acquire approximately your buyers, items, and site clients might help not just to interpret your prior, it will probably additionally assist you are expecting your destiny! Probabilistic programming makes use of code to attract probabilistic inferences from info. through using really expert algorithms, your courses assign levels of likelihood to conclusions. this suggests you could forecast destiny occasions like revenues tendencies, laptop approach disasters, experimental results, and lots of different serious issues.
About the Book
Practical Probabilistic Programming introduces the operating programmer to probabilistic programming. during this booklet, you’ll instantly paintings on sensible examples like construction a unsolicited mail filter out, diagnosing desktop approach info difficulties, and improving electronic photographs. You’ll realize probabilistic inference, the place algorithms help in making prolonged predictions approximately concerns like social media utilization. alongside the best way, you’ll discover ways to use functional-style programming for textual content research, object-oriented versions to foretell social phenomena just like the unfold of tweets, and open universe versions to gauge real-life social media utilization. The booklet additionally has chapters on how probabilistic types will help in choice making and modeling of dynamic platforms.
- Introduction to probabilistic modeling
- Writing probabilistic courses in Figaro
- Building Bayesian networks
- Predicting product lifecycles
- Decision-making algorithms
About the Reader
This ebook assumes no earlier publicity to probabilistic programming. wisdom of Scala is useful.
About the Author
Avi Pfeffer is the valuable developer of the Figaro language for probabilistic programming.
Table of Contents
- Probabilistic programming in a nutshell
- A fast Figaro educational
- Creating a probabilistic programming program
- Probabilistic versions and probabilistic courses
- Modeling dependencies with Bayesian and Markov networks
- Using Scala and Figaro collections to accumulate types
- Object-oriented probabilistic modeling
- Modeling dynamic structures
- The 3 principles of probabilistic inference
- Factored inference algorithms
- Sampling algorithms
- Solving different inference projects
- Dynamic reasoning and parameter learning