An Introduction to Probabilistic Programming by Jan-Willem van de Meent, et al.
An Intro to Probabilistic Programming - Table of Contents
- 1. Introduction
- 2. A Probabilistic Programming Language Without Recursion
- 3. Graph-Based Inference
- 4. Evaluation-Based Inference I
- 5. A Probabilistic Programming Language With Recursion
- 6. Evaluation-Based Inference II
- 7. Advanced Topics
- 8. Conclusion
What You Will Learn in An Intro to Probabilistic Programming
An Introduction to Probabilistic Programming by Jan-Willem van de Meent, Brooks Paige, Hongseok Yang, and Frank Wood is a modern textbook that introduces the foundations of probabilistic programming and probabilistic machine learning. The book explains how probability theory and programming languages can work together to model uncertainty and build intelligent computational systems. It is designed for students and researchers interested in artificial intelligence and advanced machine learning methods.
The book covers important topics such as Bayesian inference, graphical models, Markov Chain Monte Carlo, Sequential Monte Carlo, and higher-order probabilistic programming languages. It explains how probabilistic models are created and how inference algorithms are used to analyze uncertain data. The material also explores the relationship between machine learning, deep learning, and automatic probabilistic reasoning systems.
One of the major strengths of this book is its balance between theory and practical implementation. It not only explains how to use probabilistic programming systems, but also how these systems are designed internally. Overall, the book provides a strong foundation in Bayesian statistics, artificial intelligence, and modern computational probability, making it a valuable resource for advanced students, researchers, and developers working in intelligent data-driven systems.
Book Details & Specifications
Title:
An Introduction to Probabilistic Programming by Jan-Willem van de Meent, et al.
Publisher:
arXiv (Cornell University)
Year:
2018
Pages:
301
Type:
PDF
Language:
English
ISBN-10 #:
B08NXRKW9W
ISBN-13 #:
978-1108805742
License:
Arxiv License
Amazon:
Amazon
About the Author: Jan-Willem van de Meent
The author Jan-Willem van de Meent
, Brooks Paige, Hongseok Yang, and Frank Wood are researchers in probabilistic programming, machine learning, and artificial intelligence. Their work focuses on combining probability theory with programming languages to build systems that handle uncertainty and perform automated inference.
Their expertise includes Bayesian inference, probabilistic programming systems, programming languages design, and deep generative models. Together, they contribute to advancing AI research, statistical modeling, and modern machine learning methods used for intelligent and data-driven systems.
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