Robert E. (Rob) Kass is Professor in the Department of Statistics, the Machine Learning Department, and the Center for the Neural Basis of Cognition at Carnegie Mellon University. Since 2001 his research has been devoted to statistical methods in neuroscience. Together with Emery Brown he has organized the highly successful series of international meetings, Statistical Analysis of Neural Data (SAND).

Uri T. Eden is Associate Professor in the Department of Mathematics and Statistics at Boston University. He received his Ph.D. in the Harvard/MIT Medical Engineering and Medical Physics program in the Health Sciences and Technology Department. His research focuses on developing mathematical and statistical methods to analyze neural spiking activity, using methods related to model identi cation, statistical inference, signal processing, and stochastic estimation and control.

Emery N. Brown is Edward Hood Taplin Professor of Medical Engineering, Professor of Computational Neuroscience, and Associate Director of the Institute of Medical Engineering and Science at MIT; he is also the Warren M. Zapol Professor of Anaesthesia at Harvard Medical School and Massachusetts General Hospital. He is both a statistician and an anesthesiologist. Since 1998 his research has focused on neural information processing, and his experimental work characterizes the way anesthetic drugs act in the brain to create the state of general anesthesia.

Provides a unified treatment of analytical methods that have become essential for contemporary researchers

Examples drawn from the literature are included throughout this text, ranging from electrophysiology, neuroimaging and behavior

Recommended prior knowledge is high-school level mathematics

Introduction.- Exploring Data.- Probability and Random Variables.- Random Vectors.- Important Probability Distributions.- Sequences of Random Variables.- Estimation and Uncertainty.- Estimation in Theory and Practice.- Uncertainty and the Bootstrap.- Statistical Significance.- General Methods for Testing Hypotheses.- Linear Regression.- Analysis of Variance.- Generalized Regression.- Nonparametric Regression.- Bayesian Methods.- Multivariate Analysis.- Time Series.- Point Processes.- Appendix: Mathematical Background.- Example Index.- Index.- Bibliography.