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
Gain next-level skills with Coursera Plus for $199 (regularly $399). Save now.
Gain insight into a topic and learn the fundamentals.
Intermediate level
Recommended experience
5 hours to complete
Flexible schedule
Learn at your own pace
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
Details to know
Shareable certificate
Add to your LinkedIn profile
Recently updated!
January 2026
Assessments
3 assignments
Taught in English
See how employees at top companies are mastering in-demand skills
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
Offered by
Why people choose Coursera for their career
Felipe M.
Learner since 2018
"To be able to take courses at my own pace and rhythm has been an amazing experience. I can learn whenever it fits my schedule and mood."
Jennifer J.
Learner since 2020
"I directly applied the concepts and skills I learned from my courses to an exciting new project at work."
Larry W.
Learner since 2021
"When I need courses on topics that my university doesn't offer, Coursera is one of the best places to go."
Chaitanya A.
"Learning isn't just about being better at your job: it's so much more than that. Coursera allows me to learn without limits."
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.
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.
More questions
Financial aid available,