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This book provides a unified review of analytical methods for neural data that have become essential for contemporary researchers. Illustrated with more than 100 examples drawn from the literature, ranging from electrophysiology to neuroimaging to behavior.
This book provides a thorough introduction to asymptotic inference. It begins with an elementary treatment of one-parameter statistical models and goes on to discuss basic properties of curved exponential families, the Fisher-Efron-Amari theory and Jeffreys-Rao Riemannian geometry based on Fisher information.
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