MB
5.0Reviewed Oct 25, 2016
Great course with clear instruction and a final peer-review project with clear expectations and explanations.
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."
Status: Statistical Analysis
Status: Statistical Modeling
IntermediateCourse35 hours
Featured reviews
JN
4.0Reviewed Jan 2, 2017
Theis course is substantially more difficult than the three first ones, and the material is scarce. However, I must admit that this is one of the courses I have ever learnt the most
GH
5.0Reviewed Apr 9, 2018
I like this course a lot. Explanations are clear and much of the (unnecessarily heavyweight) maths is glossed over. I particularly liked the sections on Bayesian model selection.
MP
5.0Reviewed Oct 23, 2017
Slightly math heavy at times but the practical labs were awesome. I thoroughly enjoyed the final modeling assignment as well
C
4.0Reviewed Jun 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!
MC
4.0Reviewed Jun 20, 2018
It was a good course, though I would include more coursework and exercises in R to assist with comprehending a difficult subject. Overall, good course for something that's difficult to teach.
CH
5.0Reviewed Oct 29, 2017
The course is compact that I've learnt a lot of new concepts in a week of coursework. A good sampler of topics related to Bayesian Statistics.
NR
4.0Reviewed Jul 5, 2019
This course through the material too fast. The content should have been spread out over two courses in my opinion.
KM
4.0Reviewed Jun 2, 2017
Learnt a lot. Though the subject material was hard to grasp first hand, it is good that instructor was readily available to help us through.
PA
4.0Reviewed Oct 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.
MS
4.0Reviewed Sep 9, 2019
A bit more depth in explaining conjugacy in priors and posteriors will be very helpful. A possible way would be to have more example illustrations.
KB
4.0Reviewed Jul 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.