Bayesian vs Frequentist Inference: Concepts, Contrasts, and Applications

This introductory session examines the philosophical and practical differences between Bayesian and frequentist approaches to statistical inference. We discuss how these frameworks influence model formulation, parameter interpretation, and uncertainty quantification. Through applied examples, we highlight key decisions in the modeling process and demonstrate how Bayesian methods are used in contemporary research. In addition, the talk will briefly introduce commonly used R packages for Bayesian data analysis and illustrate their role in practical implementation. The seminar is designed to be accessible to a broad audience and serves as a foundation for the subsequent talks in the series.

This seminar is a part of an online seminar series about Baysian data analysis, given by the SLU Center of Statistics. The full program for the seminar series can be seen here.

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Mohammad Ghorbani