I Preliminaries
1 General Introduction
2 Basics of R
II Data
3 Data, variables & experimental designs
4 Data Wrangling
5 Summary statistics
6 Data Visualization
III Bayesian Data Analysis
7 Basics of Probability Theory
8 Statistical models
9 Bayesian parameter estimation
10 Model Comparison
11 Bayesian hypothesis testing
IV Applied (generalized) linear modeling
12 Linear regression
13 Bayesian regression in practice
14 Categorical predictors
15 Generalized linear model
- 15.1 Generalizing the linear regression model
- 15.2 Logistic regression
V Frequentist statistics
16 Null Hypothesis Significance Testing
17 Comparing frequentist and Bayesian statistics
Appendix
A Further useful material
B Common probability distributions
- B.1 Selected continuous distributions of random variables
- B.2 Selected discrete distributions of random variables
C Exponential Family and Maximum Entropy
- C.1 An important family: The Exponential Family
- C.2 The Maximum Entropy Principle
  - C.2.1 Information Entropy
  - C.2.2 Deriving Probability Distributions using the Maximum Entropy Principle
D Data sets used in the book
E Open science practices
References

An Introduction to Data Analysis

C Exponential Family and Maximum Entropy

This chapter deals with the Exponential Family of probability distributions.