About this Course

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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."

Flexible deadlines

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Shareable Certificate

Earn a Certificate upon completion

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Intermediate Level

Approx. 34 hours to complete

English

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Skills you will gain

Bayesian Statistics
Bayesian Linear Regression
Bayesian Inference
R Programming

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Intermediate Level

Approx. 34 hours to complete

English

Could your company benefit from training employees on in-demand skills?

Try Coursera for Business

Instructors

Instructor rating

3.68/5 (51 Ratings)

Syllabus - What you will learn from this course

Content Rating78%(4,016 ratings)

Week1

Week 1

1 hour to complete

About the Specialization and the Course

1 hour to complete

1 video (Total 2 min), 4 readings

6 hours to complete

The Basics of Bayesian Statistics

<p>Welcome! Over the next several weeks, we will together explore Bayesian statistics. <p>In this module, we will work with conditional probabilities, which is the probability of event B given event A. Conditional probabilities are very important in medical decisions. By the end of the week, you will be able to solve problems using Bayes' rule, and update prior probabilities.</p><p>Please use the learning objectives and practice quiz to help you learn about Bayes' Rule, and apply what you have learned in the lab and on the quiz.

6 hours to complete

9 videos (Total 41 min), 4 readings, 3 quizzes

9 videos

The Basics of Bayesian Statistics1mConditional Probabilities and Bayes' Rule2mBayes' Rule and Diagnostic Testing6mBayes Updating2mBayesian vs. frequentist definitions of probability4mInference for a Proportion: Frequentist Approach3mInference for a Proportion: Bayesian Approach7mEffect of Sample Size on the Posterior2mFrequentist vs. Bayesian Inference9m

4 readings

Module Learning Objectives2hAbout Lab Choices10mWeek 1 Lab Instructions (RStudio)2hWeek 1 Lab Instructions (RStudio Cloud)10m

3 practice exercises

Week 1 Lab30mWeek 1 Practice Quiz20mWeek 1 Quiz30m

Week2

Week 2

7 hours to complete

Bayesian Inference

7 hours to complete

10 videos (Total 45 min), 3 readings, 3 quizzes

10 videos

Bayesian Inference1mFrom the Discrete to the Continuous5mElicitation6mConjugacy4mInference on a Binomial Proportion5mThe Gamma-Poisson Conjugate Families6mThe Normal-Normal Conjugate Families3mNon-Conjugate Priors4mCredible Intervals3mPredictive Inference4m

3 readings

Module Learning Objectives2hWeek 2 Lab Instructions (RStudio)3hWeek 1 Lab Instructions (RStudio Cloud)10m

3 practice exercises

Week 2 Lab30mWeek 2 Practice Quiz20mWeek 2 Quiz40m

Week3

Week 3

8 hours to complete

Decision Making

8 hours to complete

14 videos (Total 75 min), 3 readings, 3 quizzes

14 videos

Decision making53sLosses and decision making3mWorking with loss functions6mMinimizing expected loss for hypothesis testing5mPosterior probabilities of hypotheses and Bayes factors6mThe Normal-Gamma Conjugate Family6mInference via Monte Carlo Sampling3mPredictive Distributions and Prior Choice5mReference Priors7mMixtures of Conjugate Priors and MCMC6mHypothesis Testing: Normal Mean with Known Variance7mComparing Two Paired Means Using Bayes' Factors6mComparing Two Independent Means: Hypothesis Testing3mComparing Two Independent Means: What to Report?5m

3 readings

Module Learning Objectives2hWeek 3 Lab Instructions (RStudio)3hWeek 3 Lab Instructions (RStudio Cloud)10m

3 practice exercises

Week 3 Lab30mWeek 3 Practice Quiz16mWeek 3 Quiz40m

Week4

Week 4

8 hours to complete

Bayesian Regression

8 hours to complete

11 videos (Total 72 min), 3 readings, 3 quizzes

11 videos

Bayesian regression1mBayesian simple linear regression8mChecking for outliers4mBayesian multiple regression4mModel selection criteria5mBayesian model uncertainty7mBayesian model averaging7mStochastic exploration8mPriors for Bayesian model uncertainty8mR demo: crime and punishment9mDecisions under model uncertainty7m

3 readings

Module Learning Objectives2hWeek 4 Lab Instructions (RStudio Cloud)3hWeek 4 Lab Instructions (RStudio Cloud)10m

3 practice exercises

Week 4 Lab22mWeek 4 Practice Quiz20mWeek 4 Quiz40m

Reviews

3.8

258 reviews

5 stars
45.05%
4 stars
20.55%
3 stars
14.72%
2 stars
9.13%
1 star
10.53%

TOP REVIEWS FROM BAYESIAN STATISTICS

by CJun 11, 2018

Week 3 was too much information too soon, but week 4 was great again like the other courses in this specialisation. Learned so much, thanks!

by JSDec 22, 2016

This is the hardest courses I have taken. I hoped to have more supplemental reading materials and more practical exercises in R.

by PAOct 12, 2018

The course has seen a lot of improvement with new study materials and videos. I'd say that this is now much better than what the course was previously.

by KBJul 28, 2016

The section about Beta-Binomial Conjugate is taught very fast and unless the student is quite familiar with Beta and Gamma distributions, it makes it very difficult to follow the course.

View all reviews

Frequently Asked Questions

When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. 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.
What will I get if I subscribe to this Specialization?
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. If you only want to read and view the course content, you can audit the course for free.
Is financial aid available?
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.
What background knowledge is necessary?
We assume you have knowledge equivalent to the prior courses in this specialization.
Will I receive a transcript from Duke University for completing this course?
No. Completion of a Coursera course does not earn you academic credit from Duke; therefore, Duke is not able to provide you with a university transcript. However, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

More questions? Visit the Learner Help Center.

Bayesian Statistics