返回到 Bayesian Statistics: From Concept to Data Analysis

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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....

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

筛选依据：

创建者 Fabian S

•Jan 18, 2018

A great introduction to Bayesian Statistics for everyone who has some basic knowledge of calculus and is familiar with the fundamentals of probability theory.

创建者 Antoine N

•Aug 21, 2017

Great introduction to the Bayesian framework! The exercises are relevant and I look forward to the second part (Bayesian Statistics: Techniques and Models).

创建者 Isaac D

•Jan 20, 2017

A step by step course, designed to pay attention all the time with tons of practical examples and very clear explanations, I would definitely recommend it!!

创建者 jl b

•Jun 11, 2020

Herbert is clear, gives great examples, and is easy to follow. The question prompts are helpful, and the quizzes thoughtful and challenging. Great course.

创建者 Naveen M N S

•Sep 21, 2017

Very good course for fundamentals of Bayesian statistics. Made me understand Monte Hall problem, conditional probability, etc. in a totally different way.

创建者 Pawel R

•Oct 3, 2016

The course creates great foundations for digging deeper into more complex concepts and trying to run some Bayesian statistics on simple real life problems

创建者 Mohan R

•Dec 1, 2019

A mathematics course I really enjoyed because the instructor was actually teaching the material as best as one could without meeting the students. Great.

创建者 Laure N

•Mar 6, 2018

Thank you very much for sharing your knowledge with the public. Now I am no more afraid to face the book 'Bayesian Data Analysis' by A. Gelman et al.

创建者 Allan V d C Q

•May 7, 2020

I really enjoyed this course. Dr. Lee is a really good instructor. The materials and tests are good as well and will help you during the journey.

创建者 Thadeu F

•Jul 5, 2017

Great course. Intermediate to advanced level (at least for me). You must have good foundation in probability. If so, you will learn a lot. Thanks

创建者 Simiao R

•Jul 20, 2020

Good course about bayesian! I finally understand the relationship between frequentist idea and Bayesian approach and Beta gamma distributions

创建者 Eben E

•Apr 12, 2020

This was a were educational course. I had trouble understanding R programming but with this topic, most of the programs became more clear to me.

创建者 Cooper O

•Jun 27, 2017

A Fantastic course. Detailed learning materials, Lots of opportunities to test your knowledge, and difficult enough to make you learn something!

创建者 Nitin K

•Jun 1, 2017

I loved everything about this course. It reminded me of my time in school. Papers and pencils. I look forward to attending the follow up course.

创建者 Tiannan S

•Jul 6, 2020

As a computer science student, I feel Bayesian approach is much more intuitive and more computationally friendly than the frequentist paradigm.

创建者 Fernando D L

•Mar 11, 2019

It's a good course to know the principal concepts of Bayesian statistics. Also, the course has excellent examples to understand thew concepts.

创建者 Orfeas K

•Mar 2, 2018

I really appreciated the content, and the way it was taught by Prof. Lee. His explanations were intuitive, without loss of mathematical rigour.

创建者 Giovanni G

•Jul 29, 2020

Consistent and mathematically dense. If you want to go through every passage this course gives you solid understanding of Bayesian statistics.

创建者 RIcardo G M

•Dec 15, 2019

Very good course. Concepts are very well explained, and quizzes are really helpful to apply and further

understand the explanations provided.

创建者 Kuntal B

•Nov 13, 2019

Thanks, Coursera. This is a good course. It would be helpful if we get any proper class notes on Jeffrey's prior and Multivariate regression.

创建者 Artem B

•Jul 3, 2019

Great course with a lot of simple, but illustrative exercises. It may be useful to have some basic prior knowledge of econometrics/statistics

创建者 Michael W

•Jan 16, 2019

Great introductory course. It was challenging but doable for someone who has not take college level mathematics or statistics in a few years.

创建者 Robert K M

•Feb 11, 2018

Invaluable. Excellent quizzes. A few terms could have been better defined, and a few more examples wouldn't hurt, but overall excellent.

创建者 Damian C

•Nov 10, 2016

Very well presented course. Interesting and intuitive introduction into the fascinating Bayesian world.

Many thanks and congratulations!!!

创建者 Ariel A

•Oct 12, 2017

Great course, it has the right proportion of theory and practice. It's a great start for anyone who wants to dive into Bayesian Analysis.

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