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Avis et commentaires pour d'étudiants pour Bayesian Statistics: From Concept to Data Analysis par Université de Californie à Santa Cruz

2,913 évaluations
758 avis

À propos du cours

This course introduces the Bayesian approach to statistics, starting with the concept of probability and moving to the analysis of data. We will learn about the philosophy of the Bayesian approach as well as how to implement it for common types of data. We will compare the Bayesian approach to the more commonly-taught Frequentist approach, and see some of the benefits of the Bayesian approach. In particular, the Bayesian approach allows for better accounting of uncertainty, results that have more intuitive and interpretable meaning, and more explicit statements of assumptions. This course combines lecture videos, computer demonstrations, readings, exercises, and discussion boards to create an active learning experience. For computing, you have the choice of using Microsoft Excel or the open-source, freely available statistical package R, with equivalent content for both options. The lectures provide some of the basic mathematical development as well as explanations of philosophy and interpretation. Completion of this course will give you an understanding of the concepts of the Bayesian approach, understanding the key differences between Bayesian and Frequentist approaches, and the ability to do basic data analyses....

Meilleurs avis


31 août 2017

Good intro to Bayesian Statistics. Covers the basic concepts. Workload is reasonable and quizzes/exercises are helpful. Could include more exercises and additional backgroung/future reading materials.


16 oct. 2020

An excellent course with some good hands on exercises in both R and excel. Not for the faint of heart mathematically speaking, assumes a competent understanding of statistics and probability going in

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701 - 725 sur 751 Avis pour Bayesian Statistics: From Concept to Data Analysis

par Yuzhong W

3 oct. 2016

The lectures from week 1 to week 3 are nice and useful to me, but I think there should be more details about the content in week 4. For example, I think the lecture about the Jeffreys prior skipped many things and I did not understand this concept very well.

par Damel L

29 nov. 2019

Most of the support material should be prior reading. Lecturing could be more useful i.e. explaining ore about why we use certain distribution and how to apply them. Most of it as just reciting formulas and felt like a waste of time...

par Olexandr L

1 juil. 2017

It was quite difficult to learn from just the material provided here, and I had to look for info on the web. Also, adding modern real life examples and going into detail would make this course better

par Jesús R S

19 juil. 2017

Good course as an introduction to bayesian statistics if you want to pursue more advanced courses in the field or to get some practise working with distributions under the bayesian framework.

par Silvia Z

8 mai 2020

In general, the course is useful, but in half of videos the explanation focused mostly on formulas, and less on theory. I personally had difficulty in learning theory of Bayesian statistics.

par Borja R S

25 avr. 2020

The teachers are clearly experts in what they do, but sometimes I think it is that same expertise that makes them jump to conclusions too easily, making it difficult for beginners to follow.

par Ran W

25 juil. 2020

This course gives a very brief background on conjugate prior. However, the lectures on Bayesian linear regression is too superficial. I wish the lectures could have gone into more detail.

par Carlos

8 avr. 2020

Too much time spent on the beginning and too little on later more complicated concepts such as the posterior predictive. It felt as if that was just a side note in the extra readings.

par Augusto S P

24 sept. 2017

The course is good for beginners in statistics. In my opinion it would be better to invest more time explaining different topics about bayesian regression and bayesian time series.

par Oliver B

1 juin 2020

Solid mathematical grounding, but would have benefited from more time spent on the history of Bayesian inference, when to use it, why it can be used etc..

par Pranav H

1 juil. 2018

The course could have given more information on tiny details which can confuse people during the exercises. But overall a good learning experience

par Ángel L

4 juil. 2021

It’s ok to have a theoretical basis about Bayesian Statistics, but I missed some practical cases using Python instead of R. I also missed PYMC3

par Kathryn L

23 juil. 2021

It's a nice introduction to the topic, but I often found the lectures to be imprecise or inconsistent, especially with respect to terminology.

par Alessandra T

29 juin 2017

We still don't understand how Bayes differs to Frequentist... A worked example comparing the two at the end would have been nice.

par Ken M

1 mai 2019

It would have been great if more graphs had been provided, for easier visualization of the e.g. distributions, or concepts.

par roger

24 juil. 2019

It would be better to add more explain about those equations and connect the math stuffs with the real world samples

par Max H

14 juil. 2019

It would be much better if there was a more sufficient introduction to the various distributions used in the course.

par Victor D

9 juil. 2019

Very informative as an introduction to concepts, but nowhere near the deep dive I'm now interested in taking.

par Isra

4 mai 2020

Good course!!... Additional examples of real life explained and done in R or excel will make it great

par Andres F P A

18 juin 2021

A lot of formulas and not that much interpretation. It is a good start in Bayesian concepts.

par Binu M D

21 sept. 2019

Too much theoretical than practical applications. No need to give both R and Excel videos.

par A A

26 nov. 2018

Would have liked more problem solving and real-world application examples.


15 juin 2020

The workload is manageable however the homework is somewhat challenging.

par Hassan A

11 mai 2020

Not well organized.

No sufficient materials, references, etc.

Very short.

par sokunsatya s

31 mai 2018

Overall, it's Ok. but the explanation is too short and incomplete.