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学生对 加州大学圣克鲁兹分校 提供的 Bayesian Statistics: From Concept to Data Analysis 的评价和反馈

4.6
1,695 个评分
440 个审阅

课程概述

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....

热门审阅

GS

Sep 01, 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.

JH

Jun 27, 2018

Great course. The content moves at a nice pace and the videos are really good to follow. The Quizzes are also set at a good level. You can't pass this course unless you have understood the material.

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301 - Bayesian Statistics: From Concept to Data Analysis 的 325 个评论(共 430 个)

创建者 Andrea P

Sep 23, 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.

创建者 Venkatesh U

Dec 02, 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.

创建者 Chuck M

Jan 11, 2017

A good course - recommended.

创建者 Aaron B

Sep 14, 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.

创建者 Francisco A d A e L

Nov 30, 2016

Very good course, with less emphasis in the videos and more on exercises and critical thinking, the way I like and learn the best. I particularly liked that the lecturer writes on a transparent vertical surface standing between him and the camera, very convenient. For those not so familiar with mathematics, this might hurt a bit but the payoff is super positive.

创建者 Witold W

Aug 29, 2017

Liked it and can recommend it.

创建者 Mohammad D

May 16, 2018

I expected more examples to be solved or at least it could be provided as supplementary materials

创建者 Raman K

Apr 08, 2017

Steep learning curve. really taxed my thinking capacity. I enjoyed it thoroughly!

创建者 Spyros L

Sep 20, 2017

Very good introduction!

创建者 Gurpreet

Nov 26, 2016

A good course but neither notes nor lectures were not in much details. But still it was worth my time. I strongly recommend it if you want a subtle introduction to Bayesian Statistics.

创建者 Taylor J W

Jan 01, 2018

Very good intro to Bayesian statistics. I only rate 4/5 because the second week was disproportionately more difficult than the other three weeks.

创建者 Ali Z

Nov 22, 2016

As a grad student myself, I liked the way this course was presented in short video format and in only 4 weeks. Definitely there are much more to learn about Bayesian Statistics and one can go way deeper, but this course gives the required basic Bayesian knowledge to someone who wants to get familiar in a short time.

创建者 lai p w

Jan 01, 2018

I can learn the concept, but need to understand the details well in other ways. eg. reading, or searching online

创建者 jose a z r

Aug 28, 2017

Very helpfull course. I will use the principles taugh for other topics like machine learning. Thans for sharing.

创建者 Viachaslau B

Sep 23, 2016

The course is a great introduction into Bayesian statistic analysis. I particularly liked the detailed explanations of where the parameter formulas came from. Also a great thing, in my opinion, was to write the explanations on the glass instead of just displaying the final results. It kind of provided a sense of interactivity and made the material more digestible for a person with not such a strong background in math. It greatly smoothed the learning curve for me and kept interested and motivated to finish the course. In the end the pace accelerated a bit but was still manageable. Four weeks seems a great duration for such a course - not becoming boring and tiring. Honors tests were quite easy, I'd prefer to have a little more challenge. Overall I'd recommend the course for everyone who wants a quick introduction into Bayesian statistics. It provides a solid background for further studies.

创建者 Kamil S

Apr 29, 2018

Excellent course, but the lack of the written notes is a big minus

创建者 Muhammad Y

Jul 30, 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! :)

创建者 Seeun P

Nov 11, 2017

Very Useful

It was little difficult to understand what the professor , but anyway it was good.

创建者 xu w

Sep 03, 2017

this is a very good introductory course on Bayesian Statistics. Thought you will not learn deep from this course, it will give you a good big picture.

创建者 Jose N d l R

Apr 17, 2017

I think that, besides lesson 11 and 12, everything was very well explained. I was a bit confused with lessons 11 and 12 since I am not new to econometrics. Perhaps I found it confusing the theory background related to the lessons themselves. Just my opinion, very good course.

创建者 Edward R

Jul 09, 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!!

创建者 Megan R

Sep 24, 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.

创建者 Lucas J

Aug 28, 2017

I've always found stats kind of boring but, the material covered in this course is invaluable. Dr. Lee presents everything clearly and concisely.

创建者 Bishal L

Mar 07, 2017

It is a nice introductory course on Baysian s

创建者 David I M

Sep 19, 2017

Satisfied with the course in general. Good investment of my time!!