LearnBayes: Functions for Learning Bayesian Statistical Inference

A collection of functions for learning Bayesian statistical inference, created by Jim Albert. It contains functions for summarizing basic one and two parameter posterior and predictive distributions. The collection also includes MCMC algorithms for user-defined posteriors, plus functions for regression models, hierarchical models, Bayesian tests, and Gibbs sampling illustrations.

Use Cases

• Learn basic tenets of Bayesian inference based on the collection's educational functions.
• Summarize one and two parameter posterior distributions based on the provided summary functions.
• Implement MCMC algorithms for custom posterior distributions based on the included user-defined tools.
• Apply Bayesian methods to regression and hierarchical models based on the dedicated model functions.
• Illustrate Gibbs sampling concepts based on the included demonstration functions.

Strengths

• Created by a named author, Jim Albert, providing a point of reference.
• Covers multiple core Bayesian topics: posterior summaries, MCMC, regression, hierarchical models, and tests.
• Includes functions for both basic learning and user-defined model implementation.

Limitations

• Row count and dataset size are unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Last update date is unknown; freshness unverified.

Provenance

Source

Jim Albert

Collection Method

Collection of statistical functions for educational and practical use.