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Count Predicted Variable
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
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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
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Goals, Power, and Sample Size
14
Overview of the Generalized Linear Model
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Metric-Predicted Variable on One or Two Groups
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Metric Predicted Variable with One Metric Predictor
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Metric Predicted Variable with Multiple Metric Predictors
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Metric Predicted Variable with One Nominal Predictor
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Metric Predicted Variable with Multiple Nominal Predictors
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Dichotomous Predicted Variable
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Nominal Predicted Variable
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Ordinal Predicted Variable
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Count Predicted Variable
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Tools in the Trunk
References
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Count Predicted Variable
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Ordinal Predicted Variable
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Tools in the Trunk