6
Inferring a Binomial Probability via Exact Mathematical Analysis
Doing Bayesian Data Analysis in Julia using Turing.jl
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What and why
1
What’s in This Book (Read This First!)
2
Introduction: Credibility, Models, and Parameters
3
The Julia programming language
4
What is This Stuff Called Probability?
5
Bayes’ Rule
6
Inferring a Binomial Probability via Exact Mathematical Analysis
7
Markov Chain Monte Carlo
8
Turing.jl
9
Hierarchical Models
10
Model Comparison and Hierarchical Modeling
11
Null Hypothesis Significance Testing
12
Bayesian Approaches to Testing a Point (“Null”) Hypothesis
13
Goals, Power, and Sample Size
14
Overview of the Generalized Linear Model
15
Metric-Predicted Variable on One or Two Groups
16
Metric Predicted Variable with One Metric Predictor
17
Metric Predicted Variable with Multiple Metric Predictors
18
Metric Predicted Variable with One Nominal Predictor
19
Metric Predicted Variable with Multiple Nominal Predictors
20
Dichotomous Predicted Variable
21
Nominal Predicted Variable
22
Ordinal Predicted Variable
23
Count Predicted Variable
24
Tools in the Trunk
References
6
Inferring a Binomial Probability via Exact Mathematical Analysis
5
Bayes’ Rule
7
Markov Chain Monte Carlo