• Title: Probabilistic Programming for Procedural Modeling and Design
• Author(s) Daniel Ritchie
• Publisher: Stanford Univercity
• Paperback: N/A
• eBook: PDF
• Language: English
• ISBN-10/ASIN: N/A
• ISBN-13: N/A
• Share This:  

Book Description

This book use probabilistic programming languages to express such Bayesian procedural models. A probabilistic programming language (PPL) provides random choice and Bayesian conditioning operators as primitives, and inference corresponds to searching the space of program executions for high-probability execution traces.

• N/A

• Amazon

• Probability and Stochastic
• Statistics, Mathematical Statistics
• Bayesian Thinking
• Python Programming
• Computational and Algorithmic Mathematics

• Probabilistic Programming for Procedural Modeling and Design (Daniel Ritchie)
• The Mirror Site (1) - PDF

• Foundations of Probabilistic Programming (Gilles Barthe, et al.)

  This open access book provides a state-of-the-art overview of the theoretical underpinnings of modern Probabilistic Programming and their applications in machine learning, security, and other domains, etc.

• Bayesian Methods for Hackers: Probabilistic Programming

  This book illuminates Bayesian inference through probabilistic programming with the powerful PyMC language and the closely related Python tools NumPy, SciPy, Matplotlib, through practical examples and computation - no advanced mathematics required.

• An Introduction to Probabilistic Programming

  This book is an introduction to Probabilistic Programming. It not only provides a thorough background for anyone wishing to use a probabilistic programming system, but also introduces the techniques needed to design and build these systems.

• Probabilistic Machine Learning: An Introduction (Kevin Murphy)

  This book is a comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach. It is written in an informal, accessible style, complete with pseudo-code for the most important algorithms.

• Bayes Rules! An Introduction to Applied Bayesian Modeling

  An engaging, sophisticated, and fun introduction to the field of Bayesian statistics, it brings the power of modern Bayesian thinking, modeling, and computing to a broad audience. Integrates R code, including RStan modeling tools, bayesrules package.

• O'Reilly® Think Bayes: Bayesian Statistics in Python

  If you know how to program, you're ready to tackle Bayesian statistics. With this book, you'll learn how to solve statistical problems with Python code instead of mathematical formulas, using discrete probability distributions rather than continuous mathematics.

• Bayesian Reasoning and Machine Learning (David Barber)

  This practical introduction is ideally suited to computer scientists without a background in calculus and linear algebra. You'll develop analytical and problem-solving skills that equip them for the real world. Numerous examples and exercises are provided.

• Bayesian Methods for Statistical Analysis (Borek Puza)

  Bayesian methods for statistical analysis is a book on statistical methods for analysing a wide variety of data. It contains many exercises, all with worked solutions, including complete computer code.

• Bayesian Data Analysis (Andrew Gelman, et al.)

  This classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. It takes an applied approach to analysis using up-to-date Bayesian methods.

• An Introduction to Bayesian Thinking (Merlise Clyde, et al.)

  This book provides an introduction to Bayesian inference in decision making without requiring calculus. It may be used on its own as an open-access introduction to Bayesian inference using R for anyone interested in learning about Bayesian statistics.

• Bayesian Networks and BayesiaLab (Stefan Conrady, et al.)

  This practical introduction is geared towards scientists who wish to employ Bayesian Networks for applied research using the BayesiaLab software platform. It can serve as a self-study guide for learners and as a reference manual for advanced practitioners.