Master Bayesian Analysis in R: 2024 Applied Statistics Guide
Focused View
1:12:24
1 - Why Bayes Introduction and Welcome.mp4
05:28
2 - Bayes Theorem.mp4
09:58
3 - Bayesian Priors in Detail and a Little About Sampling.mp4
08:13
4 - Bayesian Regression in R.mp4
23:20
5 - Logistic Regression and Predictions.mp4
11:15
6 - Diagnostics and Validation.mp4
09:39
7 - Practical Tips and Conclusions.mp4
04:31
More details
Course Overview
Transition from frequentist to Bayesian statistics with this practical R-based course covering modeling, regression, and real-world data analysis.
What You'll Learn
- Contrast Bayesian and frequentist statistical approaches
- Implement complete Bayesian workflows in R
- Build predictive models using Bayesian regression
Who This Is For
- Data analysts transitioning to Bayesian methods
- Researchers implementing statistical modeling
- R users expanding their analytical toolkit
Key Benefits
- Practical Bayesian implementation without heavy theory
- Hands-on R examples for immediate application
- Focus on interpretable results for decision-making
Curriculum Highlights
- Bayesian Foundations & R Setup
- Model Specification & Workflows
- Bayesian Regression & Prediction
Focused display
Category
- language english
- Training sessions 7
- duration 1:12:24
- Release Date 2025/05/10
