4.6
2,915 个评分
758 条评论

## 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

Aug 31, 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.

JB

Oct 16, 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

## 726 - Bayesian Statistics: From Concept to Data Analysis 的 750 个评论（共 752 个）

May 31, 2018

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

Aug 24, 2017

better to come up with more examples and more mathematical details

Jan 1, 2019

This course could be taught in better understanding way

Jun 3, 2019

For some derivations, the explanations are too sparse.

May 31, 2021

thank you my teacher

Apr 9, 2017

A bit too short.

Jan 3, 2021

I expected better teaching quality. The instructor is undoubtedly one of the bests in his area, but I personally did not like his teaching in this course. I felt he knows a lot of interesting concepts but intentionally does not teach them. The whole course was like somebody was reading from a textbook without adding any comments for students to actually grasp the concepts. In general I liked the course but I expected to learn much more from it.

Jul 28, 2019

It's alright because it gives you an overview of what is covered in a Bayesian Stats class, but the material is presented quite poorly and I had to do a lot of second hand reading to answer the questions. It is not particularly enlightening even and the formulas are presented without proper grounding, context, and intuition. I can recommend this only for dedicated self-studiers who already have some sort of grounding in Bayesian reasoning.

Apr 26, 2021

Week 4 explanations are just theoretical where professor is literally not giving any intuition and rushing through the concepts with equations which did not make any sense to me. Till week 3 I could kind follow. I did this course with the intend of giving a based for Machine Learning study and I am an thoroughly disappointed the way it ended up.

Dec 31, 2020

Good content. However, way of presentation is not very engaging. Presenter's voice very monotonous and free of any engagement. In my opinion, scribbling formulas to the board does not make a helpful learning experience.

Jun 17, 2021

A pretty standard "college-like" course with many definitions and derivations that do not help with conceptual understanding of the material. There are better tutorial/explanation videos on YouTube.

Jun 23, 2021

U​nsuficient explanation.

Lecturer just writes formulas without trying to explain background concepts. It is like reading a book of statistics. No way that most of the students will understand it.

Feb 2, 2017

Some matters were just given formulas and there was a lack of practice. The course should cover less materials or be longer to be effective in teaching.

Sep 7, 2020

Disappointing. Hard to follow, as concepts are not fully explained or linked. Steps in equations are often skipped without notice.

Jul 3, 2019

it was an okay course, I liked that they used R occasionally in the course, but I did not like how the concepts were discussed

May 1, 2017

Not very much in depth and does not offer complete lecture notes, which are necessary for answering the quizzes...

Aug 29, 2020

Dear Sir, My assignment Quizzes are locked and i am not able to unlock that. Kindly unlock it and help me out.

Jul 12, 2020

Materials could be more "worked". Blackboard Classes does not explain all what they should.

Jun 22, 2020

Ok overview, but not detailed enough to get a thorough understanding

Aug 16, 2020

I don't learn new things. It wasn't as good as I expected.

Nov 27, 2019

I would have given this course a zero rating if I could have. The worst online course I had so far. There is no intuition of the subject provided. The instructor just looks like reading from a text (like a robot) and write some equations without enough explanation. There are many Youtube videos available for free that explain concepts way much better than what is available here. Don't waste your time. Reading a book and watching those Youtube videos would help you more.

Jan 29, 2017

I have studied some Bayesian Statistics before. I feel like the materials itself is not sufficient for entry level, and will actually confuse some of the learners. Anyway, this is just my two cents. :)

Sep 22, 2020

Instructor hardly spends time in discussing the concepts. Too much focused on example but without context it does not help.

Mar 20, 2020

The teacher has diminished educational qualifications. The topics are simply outlined without further elaboration.

Jan 17, 2021

I needed to rely on other youtube videos explaining these concepts to progress in this course