# Recommended books

## Contents

### Numerical Recipes

There is no required text. However, many lectures will utilize methods in Numerical Recipes, Third Edition. Enrolled students will be provided with a free electronic subscription to this book, as well as access to its source code.

### Other Recommended Books

Some other relevant books, roughly in priority order, are:

Bishop, Pattern Recognition and Machine Learning Closest book that I know of to the spirit of this course, although the order of presentation is quite different.

Hastie, Tibshirani, and Friedman, The Elements of Statistical Learning Probably the best-selling book in machine learning.

Gelman et al., Bayesian Data Analysis

Ewens and Grant, Statistical Methods in Bioinformatics

Givens and Hoeting, Computational Statistics

Wasserman, All of Statistics This is a good book for learning the "jargon" of the statistics literature.

Sivia and Skilling, Data Analysis: A Bayesian Tutorial A good book on both the fundamentals of Bayesian thinking and applications.

### Susan Holmes

Professor Susan Holmes at Stanford has put course notes and materials on-line for several courses that are related to this one. You'll have to search around to find topics that we cover, but it is usually worth the effort. The starting point is here.

### Other On-line books (varying quality)

You can find some on-line statistics texts that other instructors have made available. These might be useful, though I am not recommending any in particular. (Add additional links here as you find them.)

Experimental Design and Analysis, Howard J. Seltman Course at CMU.

Virtual Laboratories in Probability and Statistics University of Alabama at Huntsville