Practical probabilistic programming
Summary Practical Probabilistic Programming 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 im...
| Otros Autores: | |
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| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Shelter Island, New York :
Manning Publications
[2016]
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| Edición: | 1st edition |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009630204006719 |
Tabla de Contenidos:
- Intro
- Copyright
- Brief Table of Contents
- Table of Contents
- Foreword
- Preface
- Acknowledgements
- About this Book
- About the Author
- About the Cover Illustration
- Part 1. Introducing probabilistic programming and Figaro
- Chapter 1. Probabilistic programming in a nutshell
- Chapter 2. A quick Figaro tutorial
- Chapter 3. Creating a probabilistic programming application
- Part 2. Writing probabilistic programs
- Chapter 4. Probabilistic models and probabilistic programs
- Chapter 5. Modeling dependencies with Bayesian and Markov networks
- Chapter 6. Using Scala and Figaro collections to build up models
- Chapter 7. Object-oriented probabilistic modeling
- Chapter 8. Modeling dynamic systems
- Part 3. Inference
- Chapter 9. The three rules of probabilistic inference
- Chapter 10. Factored inference algorithms
- Chapter 11. Sampling algorithms
- Chapter 12. Solving other inference tasks
- Chapter 13. Dynamic reasoning and parameter learning
- Appendix A. Obtaining and installing Scala and Figaro
- Appendix B. A brief survey of probabilistic programming systems
- Index
- List of Figures
- List of Tables
- List of Listings.
