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# Avis et commentaires pour d'étudiants pour Statistiques bayésiennes par Université Duke

3.8
étoiles
767 évaluations
248 avis

## À propos du cours

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."...

## Meilleurs avis

RR
20 sept. 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
9 avr. 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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## 226 - 241 sur 241 Avis pour Statistiques bayésiennes

par Santiago R

16 sept. 2020

The material has not enough contextualization. The explanations are way to superficial. Its not necessary to explain everything, but even the intuition is lost. The teachers dont help: except from Çetinkaya-Rundel the others read from a telemprompter and one even has to wonder if they know what theyre saying. It seems that theyre more worried to dont loose the pace of the teleprompter than to convey meaning.

par Ilya P

13 sept. 2017

While the first 3 courses had ample examples, guided practices, and other tools to learn, this course does not. Quizzes do not have good explanations, and videos do not have guided practice. There is no book to follow, hence, learning the material is difficult.

Instructors need to rework the course to include books, guided practices, and guided R examples in order to aid comprehension.

par Ben R

8 avr. 2018

A frustrating course, especially when compared to the other courses in this specialization. Lectures alternated between over my head and not giving enough information. Projects seemed designed for someone with a better grasp of R. I will probably look for another course on Bayesian statistics, because I feel my grasp of these concepts is still weak.

par Michael F

21 sept. 2020

The information felt purely academic. I know we were show how professionals have used this type of analysis before, but those examples were way more advanced than the scope of this course. Moreover, the scope of the course was too broad. More information on how to model non-linear data would have been more valuable than this.

par Andrew B O

11 août 2017

The change of instructors negatively affected this class. The new instructors are nowhere near as good at explaining the data and tending to start talking about things without even explaining what they where to to use a lot of activations, which one would need to continually look up.

par Naren T

26 déc. 2019

Very poor explanation in week 3, the new professor is not explaining the definitions or the use of them properly. Too many jargons.

Professor doesnt explain the use of prior predictive distribution and just introduces the formula without any consideration for explanation

par Yu-Chi B

12 oct. 2020

No efforts on maintaining the quality of assignment. You will be hard or never to finish them.

Too much information concentrated in one course without clear elaboration. It should be separated to 2~3 courses.

par QIAN Y

29 juil. 2016

The course lacks of explanation and it's very difficult to follow. It seems that the instructor just reads the slides without reasoning and explanation. Suggested reading materials are needed.

par Vishnu

30 juin 2019

A huge leap from the other courses in the specialization, which are all extremely well-constructed. Terms are not introduced and explained properly, and the whole course seems very haphazard.

par Cosma A

15 févr. 2018

1St problem speed of teaching, also other students complained

2With such a speed, material was too condensed for such a broad subject

3Not sufficient explanations for a statistics beginner

par Tom D

5 août 2016

This course is not well-presented. Lectures are unimaginative, and there isn't enough supporting material or readings.

par Paul J

2 juil. 2017

Quizzes are not related to videos. There is very limited practice problems (the best way to learn math subjects).

par Chen Z

26 oct. 2016

I get really frustrated when the tutor doesn't explain lots of concept/symbols in the materials.....

par Ashish C

29 août 2019

The quality of teaching was drastically down as compared to other courses.

par Jeffrey W

2 juin 2018

Unclear information, too vague, incomplete presentation of ideas.

par Shubham J

15 sept. 2019

becomes too much confusing at times.