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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 Ángela D C

•Jun 12, 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!

von Emmanouil K

•Aug 16, 2017

This is a very interesting topic. Lectures in weeks 3 and 4 could use some work.

von Chen

•Jul 07, 2017

It was nice learning all the distribution functions and Bayesian statistics. However, I have one suggestion: When going through equations, it's better to dive a little deeper into them, or at least go through a few steps of derivation, rather than just show them on the screen. For example, in 'Bayesian Regression' when introducing 'conjugate bivariant normal-gamma distribution, it was directly given three correlations on the screen: (1) alpha | sigma^2 ~ N(a0, sigma^2 S_alpha, (2) beta | sigma^2 ~ N(b0, sigma^2 S_beta), (3) 1/(sigma^2) ~ G(mu_0/2, mu_0 sigma^2/2. There are many terms in the equation. It would be more learner friendly if one can at least go through what term corresponding to what. Or if time is a constraint one can at least show some reasonable reference, so that learners can search for papers. I had to do quite an amount of googling to get through these things.

von Kian B

•Jul 29, 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.

von Vicken A

•Dec 29, 2016

Bayesian stats is a broad topic. Learners would benefit from more material.

von Hanyu Z

•Dec 08, 2016

The material is good. However, there is no support from the instructors to answer our questions in the discussion forum.

von George G R

•May 06, 2017

The classes are good.

von sohini m

•Oct 27, 2017

It was nice

von Stanley R C

•Jan 30, 2018

The instructors have great expertise, but this course is pretty difficult for a Bayesian newbie. Additional study guides would be helpful (especially week 4).

von José M C

•Mar 22, 2017

Good content but sometimes it gets confusing.

von Adam A

•Aug 25, 2017

An interesting and challenging course, would be better with more real examples and explanation as some of the material felt rushed

von Jae S P

•Jul 18, 2017

This is one of many good courses that one can get a glimpse of Bayesian statistics though it lacks of thorough explanation of mathematical background and reading materials of any kind.

von Mark F C

•Jun 21, 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.

von Lalu P L

•Jun 02, 2019

The course could have been more comprehensive and less verbose. It had so much content in a tiny course. Content should be less and more comprehensive.

von Uira d M

•Jul 03, 2019

The course is well structured but the span of topics is large and the complexity great. Maybe an extended version with more explanations and demonstrations of the equations would be better for understanding the whole concept of bayesian statistics, specially inference.

von Niels R

•Jul 06, 2019

This course through the material too fast. The content should have been spread out over two courses in my opinion.

von Ravichandran V

•Aug 06, 2019

Its really hard for me to follow this specific course, its as if I am reading a summary of a novel rather than a novel, ideally this course should be broken into two courses and made into two five week courses. I may need to take additional courses or read some books to get a clear understanding.

In the previous three courses the open stats book helped a lot, however, the online content for this course is difficult to follow as well.

von Stefan H

•Mar 16, 2019

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

von Santiago S

•Jul 15, 2018

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

von Pedro M 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.

von Thomas J H

•Aug 07, 2017

This course has a much steeper learning curve than the first three, and goes from theory to examples in action rather than vice versa. I think the Professors involved are super-smart and more than just qualified, but the teaching method is a noted departure from the first three courses in this series. Think this would work better as two courses. Slow things down a bit, and give more R exercises and examples.

von Kshitij T

•Jan 04, 2018

tough course.

von Tony M

•Oct 23, 2016

I found some of the instructional videos a bit confusing. It was difficult to understand the underlying methodology of some of the concepts explained. I believe the instructors assumed the students had a more rigorous understanding of the underlying calculus than was suggested for this course.

von Yang X

•Dec 04, 2016

Good course, but need more details.

von Andreas Z

•Mar 27, 2018

This introduction to Bayesian statistics familiarises you with the fundamental concepts. The difficulty is that the material covered is non-trivial and probably cannot be squeezed into the time allocated. Is is very difficult to follow the lectures and not getting lost. Thus, you need to take lot of time and maybe complement this course additional ones in order to understand the material and profit from it.

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