rmcelreath/stat_rethinking_2022
Stat Rethinking 2022
This project is a collection of Bayesian statistics courseware and educational resources. It provides instructional materials, problem sets, and solutions designed for learning Bayesian data analysis and causal modeling.
The repository includes a suite of statistical data visualization scripts used to generate instructional animations and plots. It also contains code examples that implement Bayesian modeling and survival analysis across multiple programming languages to demonstrate different computational approaches.
The materials cover a range of statistical capabilities, including causal inference for evaluating treatment effects and post-stratification weighting to adjust sample data for population representation. Other covered areas include survival analysis for modeling event durations and the application of curated statistical packages.
Features
- Bayesian Estimation Guides - Provides structured educational resources and code implementations for mastering Bayesian data analysis and causal modeling.
- Bayesian Statistics Courseware - Provides a comprehensive collection of instructional materials, problem sets, and solutions for learning Bayesian statistics.
- Causal Inference Tools - Provides tools and educational content for identifying cause-effect relationships in observational data.
- Causal Effect Estimators - Implements methods for estimating the magnitude of causal effects and generalizing those effects across populations.
- Exercise-Solution Pairings - Provides educational structures that link specific problem statements directly to verified algorithmic implementations.
- Curriculum Sequencing - Arranges learning materials in a structured sequence to guide the progression from basic to complex causal modeling.
- Course Assignments - Offers a set of structured assignments and solutions for practicing Bayesian data analysis techniques.
- Problem Set Collections - Provides structured problem sets and corresponding solutions to support the study of Bayesian data analysis.
- Bayesian Statistical Modeling - Implements probabilistic models with priors and likelihoods across multiple languages to explore computational approaches.
- Cross-Language Implementations - Provides Bayesian model implementations across multiple programming languages to demonstrate various computational approaches.
- Study Guides - Offers educational resources and study guides focused on evaluating treatment effects and generalizing outcomes.
- Causal Effect Estimators - Provides methods for estimating the magnitude of causal relationships and evaluating treatment effects from observational data.
- Survival Analysis - Provides instructional materials and code for modeling the time until a specific event occurs.
- Instructional Animation Scripts - Ships scripts that render instructional animations and plots to visualize complex statistical analysis concepts.
- Statistical Data Visualizations - Uses visual representations and plots to analyze and communicate relationships and patterns within statistical data.
- Cross-Language Implementation Maps - Provides collections of identical logic across different languages to illustrate syntax and runtime differences for educational purposes.
Star history
How this analysis was created: This summary and feature list were written by an AI model that read the project's README and public documentation pages. Each feature links to the documentation it came from; stars, license and language come straight from the GitHub API. The model does not read the source code, and the analysis is refreshed when the project is re-analysed. Learn more on our About page.
