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Analysis of Neural Data

Part of the Springer Series in Statistics Book Series

DOI: 10.1007/978-1-4614-9602-1

ISBN: 978-1-4614-9601-4

Publisher: Springer New York - Springer Journals

Nov 8, 2013

1 - 10 of 19 Chapters

LitStream

Chapter 1Introduction

Kass, Robert E.; Eden, Uri T.; Brown, Emery N.

Pages: 1 –22

[The brain sciences seek to discover mechanisms by which neural activity is generated, thoughts are created, and behavior is produced. What makes us see, hear, feel, and understand the world around us? How can we learn intricate movements, which require continual corrections for minor variations...
LitStream

Chapter 2Exploring Data

Kass, Robert E.; Eden, Uri T.; Brown, Emery N.

Pages: 23 –35

[Data analysis involves both manipulation, via formulas and computations, and interpretation of the results. During the period immediately following World War II, particularly in the United States, statistical theory was consumed with the logic of statistical inference and decision-making....
LitStream

Chapter 3Probability and Random Variables

Kass, Robert E.; Eden, Uri T.; Brown, Emery N.

Pages: 37 –69

[Probability is a rich and beautiful subject, a discipline unto itself. Its origins were concerned primarily with games of chance, and many lectures on elementary probability theory still contain references to dice, playing cards, and coin flips. These lottery-style scenarios remain useful...
LitStream

Chapter 4Random Vectors

Kass, Robert E.; Eden, Uri T.; Brown, Emery N.

Pages: 71 –104

[In most experimental settings data are collected simultaneously on many variables, and the statistical modeling problem is to describe their joint variation, meaning their tendency to vary together.]
LitStream

Chapter 5Important Probability Distributions

Kass, Robert E.; Eden, Uri T.; Brown, Emery N.

Pages: 105 –135

[In Chapter 1 we said that a measurement is determined in part by a “signal” of interest, and in part by unknown factors we may call “noise.” Statistical models introduce probability distributions to describe the variation due to noise, and thereby achieve quantitative expressions of knowledge...
LitStream

Chapter 6Sequences of Random Variables

Kass, Robert E.; Eden, Uri T.; Brown, Emery N.

Pages: 137 –148

[One of the great ideas in data analysis is to base probability statements on large-sample approximations, which are often easy to obtain either analytically or numerically.]
LitStream

Chapter 7Estimation and Uncertainty

Kass, Robert E.; Eden, Uri T.; Brown, Emery N.

Pages: 149 –178

[The examples in previous chapters, involving experimental settings ranging from human and animal behavior, to neuroimaging, EEG and EMG, neural spike trains, and in vitro recording, have illustrated the way statistical models describe regularity and variability of neural data.]
LitStream

Chapter 8Estimation in Theory and Practice

Kass, Robert E.; Eden, Uri T.; Brown, Emery N.

Pages: 179 –220

[In Section 7.2.1 we showed how the method of moments may be used to estimate the parameters of a \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs}...
LitStream

Chapter 9Propagation of Uncertainty and the Bootstrap

Kass, Robert E.; Eden, Uri T.; Brown, Emery N.

Pages: 221 –246

[At the beginning of this book we said that we wanted to lay out the key features of what we called, “the statistical paradigm,” which consists of broadly applicable concepts that guide reasoning from data in diverse contexts. One of its foundations is the idea that data may be used to express...
LitStream

Chapter 10Models, Hypotheses, and Statistical Significance

Kass, Robert E.; Eden, Uri T.; Brown, Emery N.

Pages: 247 –286

[The notion ofhypothesis is fundamental to science. Typically it refers to an idea that might plausibly be true, and that is to be examined or “tested” with some experimental data. Sometimes, the expectation is that the data will conform to the hypothesis. In other situations, the hypothesis is...

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