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3.9

Оценки: 603

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Рецензии: 185

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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автор: Zhen X

•Nov 26, 2018

Provide bunches of intuition of bayesian statistics. Worthwhile to enroll!

автор: Denise L

•Aug 02, 2018

Challenging!

автор: Luis A A C

•Oct 11, 2018

Excellent course very clear explenations.

автор: hyunwoo j

•Jul 16, 2016

very useful

автор: 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.

автор: Michael B

•Oct 26, 2016

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

автор: Perry C

•Sep 25, 2017

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

автор: julieth i m s

•Nov 27, 2017

Excellent course. Both instructors are really great.

автор: Donal G

•Jan 07, 2017

Very good course.

автор: Riku L

•Dec 23, 2017

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автор: Tian Z

•Dec 14, 2017

Pretty helpful

автор: 李俊宏

•May 22, 2017

very intuitive!

автор: Himanshu D

•Feb 20, 2017

Excellent content. Gives a very different outlook of Bayesian Stats.

автор: Byeong-eok K

•Aug 01, 2017

Good

автор: 魏震

•Nov 17, 2016

Very nice introduction to bayesian statistics, the materials have some level of depth, and the tests and assignments are highly available for beginners.

автор: Yan Q

•Dec 23, 2016

very helpful, however there is no recourse or recommend information for reference textbook.

автор: Matthew L

•Jan 31, 2018

Really hard, but absolutely worth it.

автор: 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.

автор: 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.

автор: 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.

автор: 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!

автор: 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.

автор: 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.

автор: Marina Z

•Jun 27, 2017

Challenging

автор: Subrata B

•Sep 08, 2016

Excellent introductory course!

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