Probabilistic Programming and Bayesian Methods for Hackers
by Cameron Davidson-Pilon
Publisher: GitHub, Inc. 2013
This book is designed as an introduction to Bayesian inference from a computational understanding-first, and mathematics-second, point of view. The book assumes no prior knowledge of Bayesian inference nor probabilistic programming.
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Chaos and fractals have revolutionized our view of the world. This book shows examples of the artistic beauty that can arise from very simple equations, and teaches the reader how to produce an endless variety of such patterns.
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The results of cache-simulation experiments with an abstract machine for reducing combinator graphs are presented. The abstract machine, called TIGRE, exhibits reduction rates that compare favorably with previously reported techniques.
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The authors offer a tutorial on the new mathematical ideas which underlie their research. Many of the ideas in this book will be accessible to a well-trained undergraduate with a good background in mathematics and computer science.