Probabilistic Programming for Procedural Modeling and Design by Daniel Ritchie
Probabilistic Programming - Table of Contents
- 1. Introduction
- 2. Probabilistic Methods for Directable Procedural Design
- 3. Eliminating Redundant Computation in MCMC
- 4. Exploring Tightly-Constrained Design Spaces
- 5. Handling Branching Structure with SOSMC
- 6. Learning to Sample using Neural Guides
- 7. Conclusions and Future Directions
- A. C3 Speedup Experiment Details
- B. HMC Model Specifications
- C. SOSMC Proof of Correctness
What You Will Learn in Probabilistic Programming
Probabilistic Programming for Procedural Modeling and Design by Daniel Ritchie is a research-based book that explores the connection between probabilistic programming, procedural modeling, and computer graphics. The work explains how intelligent systems can generate complex visual content automatically while handling uncertainty and randomness through advanced computational methods.
The book covers important topics such as Bayesian inference, probabilistic programming languages, Markov Chain Monte Carlo, Sequential Monte Carlo, and neural-guided procedural models. It demonstrates how these techniques help create efficient procedural systems for generating realistic shapes, structures, and graphical environments. The material combines ideas from machine learning, artificial intelligence, and modern graphics research.
One of the major strengths of this work is its practical approach to computational design and procedural generation. It shows how probabilistic machine learning can improve the creation of 3D models, architectural structures, textures, and visual simulations. Overall, the book is a valuable resource for students and researchers interested in computer graphics, artificial intelligence, procedural generation, and modern probabilistic modeling techniques used in advanced digital design systems.
Book Details & Specifications
Title:
Probabilistic Programming for Procedural Modeling and Design by Daniel Ritchie
Publisher:
Stanford Univercity
Year:
2016
Pages:
115
Type:
PDF
Language:
English
ISBN-10 #:
1483247864
ISBN-13 #:
978-1483247861
License:
CC BY-NC 3.0 US
Amazon:
Amazon
About the Author: Daniel Ritchie
The author Daniel Ritchie
is an American computer scientist and researcher known for his work in probabilistic programming, computer graphics, and artificial intelligence. He earned his PhD in Computer Science from Stanford University, where he researched advanced systems for procedural modeling and intelligent visual design.
His expertise includes machine learning, Bayesian inference, procedural generation, and probabilistic programming languages. He has worked on methods such as Markov Chain Monte Carlo, Sequential Monte Carlo, and neural-guided procedural models to improve 3D content generation and modern computational design systems.
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