The article provides an overview of Bayesian analysis methods for radiocarbon dating, emphasizing the importance of statistical methods in interpreting large datasets and the calibration of radiocarbon dates. It discusses the mathematical foundations of Bayesian statistics, including Bayes' theorem, and explains how it is applied to chronological analysis. The author highlights the need for appropriate priors, especially when dealing with multiple events, and introduces the concept of uniform phase models and other distributions such as ramped, exponential, and normal distributions. The article also covers the use of constraints and groupings in modeling, the extension of these models to multiple phases, and the application of these methods in various archaeological contexts. It discusses the limitations and refinements of multiphase models, the use of cross-referencing, and specific models like deposition sequence models. Finally, it outlines the analysis methods, outputs, and diagnostics for Bayesian analyses, emphasizing the importance of Markov Chain Monte Carlo (MCMC) methods for handling complex models.The article provides an overview of Bayesian analysis methods for radiocarbon dating, emphasizing the importance of statistical methods in interpreting large datasets and the calibration of radiocarbon dates. It discusses the mathematical foundations of Bayesian statistics, including Bayes' theorem, and explains how it is applied to chronological analysis. The author highlights the need for appropriate priors, especially when dealing with multiple events, and introduces the concept of uniform phase models and other distributions such as ramped, exponential, and normal distributions. The article also covers the use of constraints and groupings in modeling, the extension of these models to multiple phases, and the application of these methods in various archaeological contexts. It discusses the limitations and refinements of multiphase models, the use of cross-referencing, and specific models like deposition sequence models. Finally, it outlines the analysis methods, outputs, and diagnostics for Bayesian analyses, emphasizing the importance of Markov Chain Monte Carlo (MCMC) methods for handling complex models.