Voltar para Bayesian Statistics

3.9

464 classificações

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143 avaliações

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."...

por RR

•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.

por GH

•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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138 avaliações

por Wei Chun Chang

•Dec 06, 2018

The materials and response from the organization are unavailable for a while and never get an answer

por Tansel Tanner Arif

•Dec 05, 2018

Unfortunately, for me, this course did not live up to my expectations in comparison to the previous 3 courses I took as part of the Statistics with R specialisation. I gave the previous 3 a full 5 stars each.

The problems I had with this course was not that my statistics knowledge was lacking or that I found it difficult. The problems were due to the robotic delivery of the material. Specially towards the end of the course. It is very clear that the instructors have a great depth of knowledge which is incompatible with the robotic delivery structure currently in place.

For example, if you use a particular technique, even if it was introduced earlier, all it takes is a brief 2-10 second statement to re-iterate. This encourages the delivery of the material to be a hybrid of both written text the instructors are reading from, as well as a more informal aura of discussion. A guideline is: 'Can you get someone off the street to read the material you wrote to the screen?'. The more this statement is false, the more amazing your course is.

Another issue I had was that the accompanying material was immense. Am I paying a subscription to read books and passages in order to understand the material? This point is also prevalent in the forums where it was raised multiple times. These books and supplementary material would be largely not required if simple commentary was in place in the videos.

E.g. We are applying a 'BIC' prior. What does this mean? Up to this point we are used to applying priors that are distributions. This means that we are approximating the posterior probabilities of the models using the maximisation of their log-likelihoods which turns out to be easier to calculate than the posterior distributions. However, as the model space grows (>25 parameters), we may need to rely on a sampling technique, these techniques which rely on posterior probabilities to traverse the model space. If I were to say this, it would take me 10 seconds but would provide so much information to the learner.

In summary, I could have read a lot of the presented material here from a text book and found it clearer which wasn't the case in the previous 3 courses. The resources were helpful and focused on interesting points. I loved the interviews at the end with experts in the field. The statsR package is great and this is a great way to showcase its capabilities. This course is OK but I think the delivery could be improved upon.

Thanks

por Zhen XIE

•Nov 26, 2018

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

por Jaime Rodríguez

•Nov 08, 2018

Theorethical backdrop is a bit excessive on an R focused course

por Anna Peters

•Oct 24, 2018

Very large drop in quality from the previous three courses in the specialization. Unlike the previous classes, there is not a quality textbook provided. What passes for a "textbook" is essentially a written re-hashing of the lectures which provides no new examples. The lectures themselves can be hard to follow and often times skip over important calculations. There are no practice problems. The labs are also less clear and there are major leaps between what is taught and what we are expected to know to solve the problems. The lack of quality resources and poor teaching coupled with the more challenging material made this course very frustrating.

por Praveen Angyan

•Oct 13, 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.

por Luis Alberto Alaniz Castillo

•Oct 11, 2018

Excellent course very clear explenations.

por Syed Salmaan Rashid

•Sep 13, 2018

I want to give 0 ratings. The worst course I have seen so far in Coursera. Horrible planning, horrible execution and makes no sense. Totally disappointed by the style of course design and delivery

por Rofi Syedul Islam

•Aug 19, 2018

Weeks 1 and 2 were perfect. Weeks 3 and 4 felt rushed. The course suddenly became very formula heavy.

por Juan Pablo Stocca

•Aug 12, 2018

I have been doing the specialization from course 1, giving 5 stars to all of them. This course had the poorest explanations in the videos. I like the content, as it is challenging but many questions come to mind which are not thoroughly discussed in the video. For example, when you do the exercises you get topics that raises questions and there is no way to clear these doubts. Sometimes in the forums, but it shouldn't be the case.

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