Bayesian Computational Analyses with R

Learn Bayesian Statistics with R: A Comprehensive Introduction

This course provides a practical introduction to Bayesian computational analysis using the powerful R software. Discover a compelling alternative to traditional "frequentist" statistics. Instead of solely relying on data samples, the Bayesian approach incorporates prior beliefs alongside observed data to estimate the most probable values and distributions of population parameters.

Who should take this course?

This course is ideal for:

• Individuals new to Bayesian modeling.
• Intermediate Bayesian students looking to deepen their understanding.
• Bayesian practitioners seeking to enhance their R skills.
• Anyone interested in understanding and applying Bayesian concepts.

What you'll learn:

This course combines hands-on practice with conceptual understanding. You'll gain proficiency in using R for Bayesian analysis while grasping the underlying principles. All course materials, including R scripts, slides, exercises, and solutions, are provided. A basic understanding of inferential statistics and probability is beneficial, but no prior R experience is required – though it is helpful!

Course Structure:

• R Fundamentals (12 Video Lessons): Get comfortable with R and R scripting using RStudio. Learn essential R commands for data manipulation and analysis.
• The Bayesian Rule: Master the core of Bayesian inference. Explore examples using discrete and beta priors, predictive priors, and beta posteriors.
• Single Parameter Models: Learn to estimate parameters like means or standard deviations using Bayesian estimation.
• Conjugate Mixtures: Understand and apply conjugate mixtures – single-parameter models where prior and posterior distributions share similar forms. Explore how mixtures allow for testing competing theories.
• Multi-Parameter Models: Dive into multi-parameter Bayesian models to estimate multiple posterior variables simultaneously (e.g., mean and standard deviation).
• Bayesian Integrals: Learn techniques for estimating probabilities through integral estimation.
• Rejection and Importance Sampling: Explore advanced methods for Bayesian computation using rejection and importance sampling techniques.
• Model Comparison & Validation: Develop skills in comparing and validating different Bayesian models.

Key Skills:

• Bayesian Modeling
• R Programming for Statistical Analysis
• Prior and Posterior Distributions
• Bayesian Inference
• Statistical Modeling
• Data Analysis

Unlock the power of Bayesian statistics with R and gain a deeper understanding of data analysis and inference.