FreeComputerBooks.com
Links to Free Computer, Mathematics, Technical Books all over the World


 Title: Probabilistic Programming for Procedural Modeling and Design
 Author(s) Daniel Ritchie
 Publisher: Stanford Univercity
 Paperback: N/A
 eBook: PDF
 Language: English
 ISBN10/ASIN: N/A
 ISBN13: 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 highprobability execution traces.
About the Authors N/A
 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
 The Mirror Site (2)  PDF

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.

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 pseudocode 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 problemsolving 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 uptodate 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 openaccess 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 selfstudy guide for learners and as a reference manual for advanced practitioners.
:






















