Doing Bayesian Data Analysis
John Kruschke
Doing Bayesian Data Analysis - A book resource
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More resources on Bayesian Statistics
seeing-theory.brown.edu
Seeing Theory is an interactive, visual resource from Brown University that teaches probability and statistics, featuring modules on Bayes' theorem, probability distributions, sampling, and Bayesian inference.
Bayesian Statistics
This course describes Bayesian statistics, in which one's inferences about parameters or hypotheses are updated as evidence accumulates. You will learn to use Bayes’ rule to transform prior probabilities into posterior probabilities, and be introduced to the underlying theory and perspective of the Bayesian paradigm. The course will apply Bayesian methods to several practical problems, to show end-to-end Bayesian analyses that move from framing the question to building models to eliciting prior probabilities to implementing in R (free statistical software) the final posterior distribution. Additionally, the course will introduce credible regions, Bayesian comparisons of means and proportions, Bayesian regression and inference using multiple models, and discussion of Bayesian prediction. We assume learners in this course have background knowledge equivalent to what is covered in the earlier three courses in this specialization: "Introduction to Probability and Data," "Inferential Statistics," and "Linear Regression and Modeling."
Bayesian Data Analysis
Bayesian Data Analysis - A book resource
Bayesian Statistics: From Concept to Data Analysis
This course introduces the Bayesian approach to statistics, starting with the concept of probability and moving to the analysis of data. We will learn about the philosophy of the Bayesian approach as well as how to implement it for common types of data. We will compare the Bayesian approach to the more commonly-taught Frequentist approach, and see some of the benefits of the Bayesian approach. In particular, the Bayesian approach allows for better accounting of uncertainty, results that have more intuitive and interpretable meaning, and more explicit statements of assumptions. This course combines lecture videos, computer demonstrations, readings, exercises, and discussion boards to create an active learning experience. For computing, you have the choice of using Microsoft Excel or the open-source, freely available statistical package R, with equivalent content for both options. The lectures provide some of the basic mathematical development as well as explanations of philosophy and interpretation. Completion of this course will give you an understanding of the concepts of the Bayesian approach, understanding the key differences between Bayesian and Frequentist approaches, and the ability to do basic data analyses.
ocw.mit.edu
MIT OpenCourseWare (OCW) offers free, openly accessible MIT course materials—lecture notes, assignments, exams, and video lectures—from a wide range of subjects, including statistics and probability. It's a solid self-paced resource to learn core concepts and explore Bayesian statistics through real MIT coursework.
Introduction to Bayesian Statistics
Introduction to Bayesian Statistics - A book resource