Back to Bayesian Statistics

stars

753 ratings

•

243 reviews

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."...

RR

Sep 20, 2017

Great course. Difficult to apprehend sometimes as the Frequentist paradigm is learned first but once you get it, it is really amazing to see the believe update in action with data.

GH

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.

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By Guillermo U O G

•May 12, 2019

I really loved the previous courses because their reading material which was very good complimented by the video lectures, nevertheless, in this course, many of the video lectures was the repetition of the main book.

By Pedro E

•Mar 15, 2018

Course is much harder to follow than previous courses. Due to change of instructors, the notation used wasn't always introduced before and is not explained. Feels rushed if you hadn't previous notions of the subject.

By Sophie G

•Jul 25, 2018

Really hard to follow and finish, especially compared to the other classes in this specialization.

The concepts might be more complex, but the way they're taught also adds to the difficulty, in my opinion.

By Marcus V C A

•Jul 6, 2020

I think the content is very good, as well as the online book and the supplementary material. But the videos for Weeks 3 and 4 could be better ... In my opinion, they should be longer and more explanatory.

By Amy W

•Apr 19, 2020

Until the last two weeks, this course was very good. The lectures in the last couple of weeks contained lots of information and not very many examples. The third week, especially, was overwhelming.

By Shaurya J S

•Mar 20, 2018

Not as good as other courses in this specialization. Most of the times the focus was to teach the method of performing a Bayesian Statistical process rather than teaching the actual concept.

By Ganesh H

•Aug 17, 2017

I felt the course ramps up from the basics way too quickly. I didn't like the pacing in the course compared to other courses in the same specialization, although I did learn a lot.

By Luv S

•May 3, 2018

Explanations not simplified as compared to the other courses in the specialisation. Very difficult to comprehend. Instructor should take more time to explain the fundamentals.

By Jennifer g e

•Apr 10, 2021

I learned a lot but i think the teachers should explain with more examples, the things they explain seem very abstract and i had to look for extra help.

By Santiago S

•Jul 14, 2018

Se trata de explicar términos matemáticamente complejos de una manera muy general y vaga dificultando el entendimiento y el aprendizaje del tema.

By Tasmeem J M

•Aug 6, 2020

This course gave me a hard time. The lectures from week 3 and 4 seemed difficult, some more resources would be helpful.

By Stephanie A

•Mar 18, 2020

Like in all courses of this specialization, the peer assignment was a real bottle-neck in the completion of the course.

By Pauline Z

•Aug 22, 2020

This is certainly a good introduction. But it did not help me to be independent on bayesian statistics

By dumessi

•Sep 7, 2019

The explaining for some bayesian methods are unclear, which make it harder for new learner to follow.

By Robert M M

•Sep 27, 2017

Slides poor compared to 3 earlier modules and instructor not as engaging. However, the labs are good.

By Stefan H

•Mar 16, 2019

Find it hard to follow the lectures. The labs and supplement material is good though.

By Kalle K

•Jun 16, 2020

A useful course, but very demanding. Many of the lectures are fast-paced.

By Gustavo S B

•Sep 17, 2017

I would recommend to include more weeks; slow down and go deeper

By Li Z

•Aug 15, 2019

Some contents are just too difficult to understand fully.

By Christopher C

•Feb 12, 2018

Very heavy information very quickly otherwise - great

By Derrick Y

•Dec 4, 2016

Good course, but need more details.

By Xinyi L

•Aug 14, 2017

not very interested

By Kshitij T

•Jan 4, 2018

tough course.

By Vivian Y Q

•Oct 12, 2017

huge jump

By Zhao L

•Aug 4, 2016

This course covers a good amount of bayesian statistics. However, the presentation/videos starting from week 2 really sucks. They change instructors for difference topics and obviously some instructors are not very good at explaining other than reading the material.

The videos skipped many medium steps that are actually very crucial for understanding the concepts. And no suggested reading materials at all either. Also the quiz are not very well designed either. For example, some quiz are much more simpler than the course material, which makes it not helpful at all to understand the course material itself. While some times it is the opposite.

The first three courses in this specialization are very good, but somehow this course are way below the quality of the previous ones.

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