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3.9

564 Bewertungen

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168 Bewertungen

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."...

Sep 21, 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.

Apr 10, 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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von Luis A A C

•Oct 11, 2018

Excellent course very clear explenations.

von EDILSON S S O J

•Jul 25, 2016

Very Nice Course! Excellent!

von hyunwoo j

•Jul 16, 2016

very useful

von Arman A

•Aug 25, 2017

Very entertaining course !!!

von Perry C

•Sep 25, 2017

I didn't understand everything but I learned more than I ever thought I would.

von julieth i m s

•Nov 27, 2017

Excellent course. Both instructors are really great.

von Matthew L

•Jan 31, 2018

Really hard, but absolutely worth it.

von Minasian V

•Aug 16, 2016

This course was the most challenging one among all courses in specialization. I wish there were more explonation of how we get smth from smth and not like " and it appears to be equal .." and so on.

I also had to watch Ben Lambert's bayesian course on YouTube to understand the material of the second week,because Prof. David Banks was not good enough in explanation.

Assistant Prof. Mine Çetinkaya-Rundel has an amazing teaching skills.

Prof. Merise Clyde is good in explonations and I understand that she tried to present a very complicated material in a simple way, but as I have already mentioned above, I wish there were more explonations of casuality of the formulas with examples and Intuition that stands behind these formulas like in Ben Lambert's videos.

Many thanks for such an amazing experience.

von Jonathan N

•Oct 23, 2016

Outstanding material. It may be the hardest level compared to the rest specialization course, since Bayes indeed have high technical level detail. But it was worth it. Great course and detailed from the instructors.

von Michael B

•Oct 26, 2016

Great course with clear instruction and a final peer-review project with clear expectations and explanations.

von Julio C L J

•Dec 19, 2017

The course was hard, because the topic is quite difficult. I missed the readings.

von Andre G L O

•Feb 05, 2017

For sure the most challenging course so far.

I'm amazed by how our statistical intuition fits with Bayesian approach and how we can get better results.

I'm eager to use this concepts in new models at my job!

von Graeme H

•Apr 10, 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.

von Andrea P

•Nov 12, 2016

Very interesting and formative. It starts from the basics (Bayes' theorem) but then it goes beyond the usual conjugate models such as Beta-binomial and Gamma-Poisson. Bayesian Linear Regression, Bayes Factors, Bayesian Model Averaging and a brief introduction to MCMC are provided. This really put me in the position of applying Bayesian Statistics to some real world application: the final test case is a good illustration. The only minus is that the part on Bayesian Hypothesis Testing (in particular Bartlett's and Lindley's paradoxes) is a bit rushed up, and not as clear as the rest of the course. All in all, a really good course, I'm glad I followed it.

von Marina Z

•Jun 27, 2017

Challenging

von 殷子涵

•Jan 06, 2017

This course is very good for bayesian statistic theory. What is more, it also teaches a lot of coding skills with R which is really useful.

von Roland

•Sep 21, 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.

von franco g

•Dec 16, 2016

This is my first course on bayesian statistics, I really like it, it was step by step, and helps to clarify lots of concepts of frequentist statistic.

von Subrata B

•Sep 08, 2016

Excellent introductory course!

von can z

•Jan 10, 2018

Great course.

von 李俊宏

•May 22, 2017

very intuitive!

von Donal G

•Jan 07, 2017

Very good course.

von Riku L

•Dec 23, 2017

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von Tian Z

•Dec 14, 2017

Pretty helpful

von Do H L

•Jul 09, 2016

I beta-tested this course and it was an amazing experience. The instructors are super engaging and upbeat, despite having to delivery a very complex subject like Bayesian statistics. The quizzes are very rich, with a nice balance of practice quizzes and graded quizzes. The practice quizzes offer very helpful explanations that can help to reinforce understanding before doing the graded quiz. Last but not least, the final project description is super exciting and thorough. I highly look forward to completing this course and get a deep introduction to Bayesian statistics.

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