Data Analysis: A Bayesian Tutorial by Devinderjit Sivia, John Skilling

Data Analysis: A Bayesian Tutorial Devinderjit Sivia, John Skilling ebook
Page: 259
Publisher: Oxford University Press, USA
Format: pdf
ISBN: 0198568320, 9780198568322

Silva, John Skilling Initially Published: 2006. Title: Bayesian Data Analysis: A Tutorial Authors: D.S. There aren't that many other people in psychology at NYU (or elsewhere) that use Mathematica. One of the strengths of this book is the author's ability to motivate the use of Bayesian methods through simple yet effective examples. We will use the data set survey for our first demonstration of OpenBUGS. And C#, with Python and IronPython interfaces. Below are the bibliographic details for the three books that I recommend, as well as links to information about them on amazon.ca: Kruschke, J. Python Data Analysis Library - http://pandas.pydata.org/ - pandas is a library providing high-performance, easy-to-use data structures and data analysis tools for the Python . "Think Stats: Probability and Statistics for Programmers" to help programmers understand and express statistical models, in particular the Bayesian statistics at the heart of many applications. His best-known work is Data Analysis: A Bayesian Tutorial published in 1996. Our lab uses Mathematica quite a bit for data analysis and building models. Org/~pf/p4.html -- Python package for phylogenetics, useful for programmatic manipulation of phylogenetic data and trees, including maximum likelihood and Bayesian inference. Genuinely accessible to beginners: • An entire chapter on Bayes' rule, with intuitive examples and emphasis on application to data and models. The book is widely used by undergraduate students of physical sciences and cited by peers and colleagues of this field. Perform Markov Chain Monte Carlo convergence analysis using CODA. Tutorial on Bayesian inference using OpenBUGS. Summary: This book was designed for undergraduates in science and engineering. The tutorial was given by Jake VanderPlas of the University of Washington who uses machine learning for astronomical data analysis. The Python module that contains all the machine learning algorithms is scikit-learn.