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.

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...

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

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.

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.

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.

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.

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.

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.

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..

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

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

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

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

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

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

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

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

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

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

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

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

The workload is manageable however the homework is somewhat challenging.

Not well organized.

No sufficient materials, references, etc.

Very short.

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