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
par Megan R•
24 sept. 2016
A great introduction. I feel like I know a lot more about bayesian statistics now. But I do mostly feel like there is quite a bit I don't know, and while I passed, I feel like there is quite a bit more I need to do to really 'get it'. The professor recommended some books in a discussion forum and I'll be going through some of those next I am sure. I also feel, looking back, I should have had some additional math preparation before starting. The calculus was vaguely familiar but with the pace of the lectures, I felt occasionally lost. I would have found it helpful if there was a quick primer on calculus to know and review at the beginning of the course. All in all great course. Loved the presentation method.
par Edward R•
9 juil. 2017
This course provides a solid overview of simple Bayesian models and common distributions used in those models. It also provides an initial understanding of conjugate prior distributions and non-informative prior distributions. The R code used in this course is very simple; easy for a beginner, but perhaps a bit simple if you are already familiar with programming in R and doing commonplace frequentist statistical analyses (regressions, ANOVA, etc). Overall, this course is definitely worth taking if you are interested in Bayesian statistics and need a good place to start. There are quite a bit of videos and supplemental materials which allow for a broadened understanding of the materials. Thanks, Dr. Lee!!
par Aaron B•
14 sept. 2017
This is a decent course that covers an important topic that I've had a trouble finding good resources for learning about.
Pros: comprehensive coverage of the topic at a high level.
Cons: not enough examples to understand what is talked about in the lectures (especially the continuous data and prior with normal distribution lectures) and to anchor the topic in its practical uses.
I recommend supplementing this course with the MIT OCW 18-05 statistics class (I actually put this on hold and did that then came back).
If this course had a lot more practice problems with fully worked out answers it would help tremendously. I understand a sequel to this class is in the works and I look forward to taking it.
par Delson B•
15 juil. 2021
This course is PERFECT during weeks 1-3, failing only in the final week. From now on I'll focus my review on improvement points. I'd appreciate much more if the regression lesson was removed, and the normal distribution lesson received more attention. I understand that a more solid mathematical background is needed, but it could be done (much better than it is already done) in the written suplementary material. IMHO, the Bayesian methods for obtaining confidence intervals is the real gold behind this course, and I intend to share this knowledge with my work peers. In summary, I would focus on this, wouldn't try to reach some "data analysis" and prefer to stick with "Bayesian inference".
par Jurriaan N•
17 déc. 2016
This course provides the student a profound understanding of the statistics behind the bayesian approach. Also, it gives some intuition for the difference between the frequentist and the bayesian approach, although that part could have been more explicit in my opinion. It would be very helpful to have more examples on the differences in using freq vs bayesian approach, the gains from using bayesian approach, examples of where the freq approach is limiting / misleading in its 'objectiveness'. More 'real life' examples instead of coin flipping examples - although easy to follow - would be very helpful as well, maybe in a consecutive course with applied bayesian statistics?
par Jon I•
13 juin 2017
An interesting introduction to Bayesian statistics and inference. Not for people with no statistical background, as it does assume you are comfortable with various distributions, expectations, variances, etc. and the 'standard' frequentist worldview (including inferential procedures such as linear regression). The material was well explained, and generally well examined, with a mixture of multiple choice understanding questions, and numeric response tasks which also serve as a very basic introduction to R (or Excel if you are crazy). It was good to see the instructor realising that a light shirt was causing problems and switching to a darker one as the videos went on!
par Larry L E•
5 oct. 2016
I enjoyed the course. My background is mathematics, but not specifically statistics, though I do have a basic understanding of elementary frequentist statistics. My goal was to understand the fundamentals and uses of Bayesian statistics, having attempted that via a couple of textbooks without much success; this time, I got it!
I do have some reservations about the course. Herbie Lee spent a huge amount of time deriving formulas and methods - a few gaps (either hand waving or 'leave it to the student to finish') would have been helpful, I think. This would leave more time for examples and applications. But the course was well worth my time and effort.
par Venkatesh U•
2 déc. 2017
This course covers most of the basics in a very good manner. I personally feel, the last week chapters especially regression do not connect the dots between the foundation that was laid and the resources provided were also not very helpful to fill that gap. For e.g I wanted to understand regression from the bayesian context, the session mostly focused on how to do regression in R and the not the internals of how to understand the mechanics behind from the bayesian stand. I will be helpful to introduce some content that helps the user to move from univariate normal distribution to multivariate normal distribution and explains some intuition behind them.
par Lukas S•
11 sept. 2017
The course itself is wonderful, and the contents are very thoughtfully selected. I'm not a particular fan of the mirror-technique they use to shoot the videos. Basically, Professor Lee stands in front of a mirror and writes onto the mirror with text markers. On the video you see both him, and the text he writes.
His body often covers the text and generally, it is hard to read. Personally, I see no need to see the professor. Rather, I would prefer a note-taking app (white background). There, old formulas could also be replaced by LaTeX text making everything much more readable, plus there would be downloadable lecture slides automatically.
par Ramon R•
1 mars 2018
I liked that the teacher put things into perspective and showed the connections between the different concepts. I deduct 1 star, because the additional material in rare: Meaning, you have to take notes in the lectures to solve the quizzes and to have something for looking things up. Furthermore, in a few lectures it was difficult to read what the teacher was writing, because he was wearing a shirt with a too bright color. (Sounds funny, but I mean this serious ;-) ) In summary, a great lecture and perfect introduction into the concepts. The quizzes are constructed in a way, that they encourage learning rather than frustration.
par Andrea P•
23 sept. 2016
The course is nice, the lectures are really clear. Professor Lee is brilliant and he often gives some excellent interpretations of Bayesian results. For example, the classic example of testing for rare diseases is explained in terms of ratio of true positives to all positives. Another example is the explanation of predictive mean for normal models, or the explanation of noninformative priors. They're all clearer than what usually found in many books. The only limit of the course is that it's strictly an introduction, thus very useful topics for applications such as hierarchical models or nonconjugate models are not covered.
par Lucas M•
18 nov. 2019
It was a very nice course that got more practical towards the end. The only thing I found a little bit confusing is the regression part, without theory videos and with practical outcomes that are exactly the same as frequentist approaches.
Don't be discouraged if you come from a background where integers and derivatives are not usual! I come from psychology and I found it a little bit hard at the beginning, but if you put effort you will get to understand almost everything. As long as you get the idea of where things like formulas are coming from and why are they done that way I think it is enough.
par Carlos L•
16 juin 2020
I really liked this course. The material is great and the structure of the course is very well organised. A possible improvement, in my opinion, would be to include more explanatory material or take more time in the videos explaining some concepts or derivations. This is why I have to search for other resources in order to grasp some concepts and I took a lot of time in order to completely grasp all the concepts in this course (roughly 10hours for each week). The last week seems a bit rushed and lacks a bit of explanation in the linear regression, non informative priors and in the normal model.
par Maxence A•
30 août 2020
Good curriculum overall, the course can be difficult for students that don't have a strong background of statistics. I found the video lectures lacking because it was mostly formulas and not much explaining. For intuition I had to consult external sources. Most of the quizzes were well designed and challenged our understanding of the subject. While I don't feel that confident in the subject i did gain a good understanding of the overall idea behing bayesian inference.
My advice would be to provide additional videos that give more insight and intuition behind thess concepts.
par Eunylson L•
8 déc. 2021
It's a great course. I definetely recommend it. It's a great course also for us to understand the mathematics of Bayesian statistics. I would say that this course is more appropriate for those who already have a proper intuition of Bayesian statistics, philosophicaly speaking. Then you should come here to formalize your understanding with math. I give 4 stars just because in some of the classes the teacher skips some important math explanations and foundations (so we can easily get lost). Also, we could have spent more time on the applications of Bayesian statistics.
par Arkady S•
7 mai 2020
Really enjoyed weeks 1-3 of the course. It was well done and I felt like I had a good grasp of the materials, and the tests reflected that. The lecturer gave good intuition of what was going on with the math. Week 4 on the other hand was a bit hectic. I didn't feel like I had a good grasp of the material or the underlying math, and lots of it was rushed through. I also didn't feel like the quizzes in week 4 helped me understand the material more. I was able to complete them correctly just by using R, with little understanding of what's going on behind the scenes.
9 juil. 2019
Most of the stuff is explained quite well and I managed to understand it. I am quite satisfied overall and I am glad I completed the course. The exercises, however, were somewhat boring. I wish there were some optional exercises that are more challenging and require you to solve more realistic problems. I also wish there were more additional materials with more in depth theory and examples of how they use these concepts for solving problems that are actually of some use. I feel like these improvements would make the course much more interesting and engaging.
par Larry B•
13 mai 2021
I liked the course. It was a lot of work. It was probably beyond my skill levels. A few more plug in the value examples would have been a great benefit to me. I did not have an recent experience in Calculus and statistics, and I had never used R. However, I got through the course with the chance to retake the quizzes multiple times. That was a good learning experience for me because I could start to figure out how to fill in the blanks, so to speak. I think some more information on course prerequisites would have been helpful.
9 nov. 2018
It was my first Bayesian course. Good introduction! However more accent should be placed on intuitive understanding rather than mathematical formalism. To be fair that the issue not only with this course, that the issue with 90% of all stat courses/books. As for me, I find mathematical formalism is hard to digest, intuitive understanding should come first ... May be it's just because of my limited knowledge of stats. I'll update my belief once I get better understanding of stats:) Thank you very much Dr Lee!
par Matúš F•
26 avr. 2020
I would highly recommend this course to everyone, who wishes to learn basics of Bayesian statistics. I very much appreciate quizzes, videos and reading material. Few things I recommend to improve: Provide reading material for the theory presented in videos, it would be helpful to have this when I will come back to material later. Also for some quizzes and questions in videos (W2 and W4) latex didn't interpret correctly, so I had to do it on my own by copying it to latex interpreter, which was irritating.
par Muhammad Y•
30 juil. 2017
The course helped me get started with Bayesian stats. This course is good if you have seem probability and stats (distributions, pdf, cdf etc.) and want to learn about the Bayesian interpretation. The course picks up pace from 3rd week and the final week seem a bit rushed. I thing more examples of explicit frequentist vs. bayesian interpretation will benefit the learners. Also, 4th week could really use some additional explanatory content. Thanks for this course, I learned something fun and useful! :)
par Paulo G S•
18 août 2020
The course is very well explained, one can learn a lot. Although, I missed more texts to guide throughout the classes. I acknowledge the option to make annotation and access the transcript of the classes was very useful, but even so I would like more material, even to summarize some of the most important content of the classes and expressions developed. Apart from that, I felt very satisfied with the course and look forward to learn more and more about Bayesian Statistics!
par Alan L•
21 mai 2020
While the concepts are pretty advanced and worthwhile to go through, I feel like there could have been more videos explaining the concepts behind the math a bit more. It would really help solidify the concepts for people who are rusty or haven't seen statistics/probability in a long time. However, this course definitely has some GREAT practice exercises (and the honors quizzes are so worth it, so DO THEM!). Overall, tremendous effort. Would recommend.
par Nurlan J•
15 avr. 2020
I learned and revised a lot of knowledge that I forgot/did not know before. Yet, the lecture videos were not well-adopted to explain what the equations really mean. The major issue is that the professor is rushing in his explanations. Perhaps, one needs to consider the negative correlation between the length of a video and the quality of the material it can capture.
Anyways, great lecture series and advanced my knowledge. Thank you!
par Erfan A•
13 juin 2017
This was a great introduction to bayesian statistic. I have background in Computer Science and Engineering but I have not yet been introduced to Bayesian Statistics. The Quizzes were where the learning was happening for me. Personally I learn the best when I code things up. I wish they had also included coding examples in Python (which is what I used for the quizzes) since that is one on the most popular languages for data science.