Table of contents
Preface
I Introduction
1
Variation and covariation
2
Data and information
3
Data graphics
4
Stratification and summary
5
Prediction
6
Simulation
II Process
7
Process, Priors & Planning
8
Case study: from purpose to result
9
Bayes’ rule
III Modeling frameworks
10
Modeling functions
11
Models that learn
12
Confounding
13
Effect size
14
Causal networks
15
Sampling variation
IV Evaluation
16
Model performance
17
Classification error
18
Cross validation
19
Partitioning variance
20
Calculating confidence intervals with resampling
21
Small Data
V Interpretation
22
DRAFT: Loss functions
23
False discovery
Chapter 12
Confounding
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