Welcome to Bayesian Statistical Concepts and Methods. In this course, you will use Bayesian methods in data analysis and modeling; work with posterior distributions, distributions without closed form, directed acyclic graphs, Markov Chain Monte Carlo algorithms; and employ R and the Stan platform for statistical modeling. You will also be introduced to Bayesian hierarchical models, which are useful for the interpretation of multi-level data (sub-group versus group).
Bayesian Statistical Concepts and Methods
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Intermediate level
Recommended experience
5 hours to complete
Flexible schedule
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What you'll learn
Participants will learn fundamentals of Bayesian concepts and methods, including Bayesian models, Bayesian networks, and Markov chain Monte Carlo.
Skills you'll gain
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Recently updated!
January 2026
Assessments
3 assignments
Taught in English
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There are 3 modules in this course
This Specialization covers the use of statistical methods in today's business, industrial, and social environments, including several new methods and applications. H.G. Wells foresaw an era when the understanding of basic statistics would be as important for citizenship as the ability to read and write. Modern Statistics for Data-Driven Decision-Making teaches the basics of working with and interpreting data, skills necessary to succeed in Wells’s “new great complex world” that we now inhabit. In this course, learners will be able to use Bayesian methods in data analysis and modeling, to work with posterior distributions, distributions without closed form, directed acyclic graphs, and Markov chain Monte Carlo algorithms, and to use R and the Stan platform for statistical modeling. Learn more about the instructors who developed this course. Read the instructor bios and review the learning outcomes for the course.
What's included
5 videos3 readings1 assignment
In Module 2, we will draw a Bayesian model as a graph and distinguish posterior distribution, posterior predictive distribution, and expected loss or cost. We will also calculate distributions without closed form, recognizing that we can use computational methods to draw from the distribution even when there's no straight-forward equation to define them. Be sure to review the learning objectives before beginning work in this module.
What's included
9 videos2 readings1 assignment
In Module 3, we will employ R and the Stan platform for statistical modeling. You will explore Bayesian methods in data analysis and modeling; work with posterior distributions, distributions without closed form, directed acyclic graphs, and Markov Chain Monte Carlo algorithms. You will also be introduced to Bayesian hierarchical models, which estimate subgroup parameters relative to the parameters of a larger parent group. Be sure to view the course introduction video and review the learning objectives before beginning work in this module.
What's included
10 videos3 readings1 assignment1 peer review
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Frequently asked questions
To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
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