Statistical Approaches to Learning and Discovery

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The primary text for the course is :

• Martin A. Tanner, Tools for Statistical Inference: Methods for the Exploration of Posterior Distributions and Likelihood Functions, third edition, Springer Series in Statistics, Springer-Verlag, New York, 1996.

Other books that we will draw material from are listed below. Several of these will be placed on reserve in the E&S library.

• L. Breiman, J. Friedman, R. Olshen, and C. Stone, Classification and Regression Trees, Wadsworth, Belmont, MA, 1984.
• Pierre Brémaud, Markov Chains: Gibbs Fields, Monte Carlo Simulation, and Queues, Springer-Verlag, New York, 1999.
• Thomas Cover and Joy Thomas, Elements of Information Theory, Wiley, New York, 1991.
• Andrew Gelman, John B. Carlin, Hal S. Stern, Donald B. Rubin, Bayesian Data Analysis, Chapman & Hall Texts in Statistical Science Series, Chapman and Hall, London, 1995.
• Vladimir N. Vapnik, The Nature of Statistical Learning Theory, second edition, Statistics for Engineering and Information Science, Springer-Verlag, New York, 1999.

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lafferty@cs.cmu.edu